What’s Your Work Fit? is back.

Our Fall 2026 season premieres today, Wednesday, September 2, at 12 PM Eastern Time.

And we’re beginning our new season with a question that sounds simple until you start thinking about what it means:

Who owns my workplace AI?

Returning to What’s Your Work Fit? is work futurist Nirit Cohen. Joining me as co-host is MorteVita Managing Principal Fran Saele.

And, as you might expect, the conversation gets spirited.

The AI question is becoming a work question

For the past few years, much of the workplace conversation around AI has focused on capability.

What can AI do?

What tasks can it perform?

How much time can it save?

How much productive capacity can it create?

Those remain important questions. But as AI becomes increasingly agentic—and increasingly embedded in the daily work of career professionals—we need to start asking a different set of questions.

Who owns the AI that helps me do my work?

Who owns what I teach it?

Who benefits from what it learns from me?

What happens when my experience, judgment, style, institutional knowledge and problem-solving approaches become part of an AI-supported workplace system?

And perhaps most important:

Where does the career professional end and the workplace AI begin?

Here’s a tease: Nirit offers that all of it will be negotiated.

And that is because our work is more than our output

For generations, career professionals built value through experience.

We learned how to manage difficult situations. We developed relationships. We figured out which questions to ask. We learned from failures. We acquired judgment and exercised discernment.

Accumulated knowledge became part of our professional value.

It helped us to earn promotions, move between employers, become independent professionals, and build careers.

But agentic AI introduces something new.

Increasingly, the systems we use won’t merely help us complete tasks. They are learning from how we complete them.

That creates an entirely new dimension to the relationship between employer and employee.

If I spend years helping an AI system become better at performing work that reflects my knowledge and experience, what happens when I leave?

Does everything the system learned stay behind?

Should some part of that accumulated capability belong to me?

Could career professionals eventually arrive at new jobs carrying their own AI agents—systems informed by their work histories, preferences, methods and accumulated professional knowledge?

And if they do, will employers welcome those agents through the door?

AI could increase agency—or reduce it

This question also connects directly to something we’ve been discussing frequently on the show: agency.

AI has the potential to give career professionals extraordinary new agency.

It can help us research faster, explore possibilities, challenge assumptions, develop ideas and remove some of the administrative debris that consumes our days.

Used well, it can help create something I’ve been calling productive capacity: the ability to accomplish necessary work more effectively while creating room for higher-value human contribution.

There’s another possible outcome.

Workplace AI could become primarily an employer-owned system designed to extract, standardize and retain human knowledge. To that end:

Career professionals may gain efficiency while simultaneously losing some control over the very expertise that makes them valuable.

That’s why “Who owns my workplace AI?” isn’t merely a technology question.

It’s a Work Fit question.

The conversation starts today

Nirit Cohen has spent years challenging conventional assumptions about where work is heading. That makes her exactly the expert we wanted to start this discussion.

Fran Saele and I join Nirit in exploring what ownership, agency and professional identity could mean as AI becomes inseparable from our working lives.

We don’t pretend to have all the answers.

In fact, I suspect we’re still discovering the questions.

What’s Your Work Fit? exists to help career professionals make work a wonderful part of their day doing other wonderful things.

Increasingly, achieving that Work Fit will require us to understand not only where we work, when we work and what we do—but also our relationship with the intelligent systems working alongside us.

So, who owns your workplace AI?

Your employer?

You?

Both?

Or are we about to discover that our old concepts of ownership no longer fit the way work is evolving?

The Fall 2026 season of What’s Your Work Fit? begins 12PM Eastern Time today, Wednesday, September 2, 2026.

Watch the new episode here when it drops:

And while you’re there, subscribe to What’s Your Work Fit? on YouTube and follow us on LinkedIn so you never miss an upcoming episode, conversation or clip.

Welcome back.

We have a lot to talk about this fall.

Our best days lie ahead.

Note: Image rendered on ChatGPT based on our prompts.

I’m Dan Smolen. As host and executive producer of What’s Your Work Fit? I help you make your work and workplace decisions result in better and more satisfying professional experiences and outcomes. I am also a Founding Member of The Future of Work Alliance.

Join What's Your Work Fit? on LinkedIn

Join What's Your Work Fit? on YouTube

For years, we described a good job through familiar value frames.

Compensation. Benefits. A good boss. Interesting work. Advancement opportunities. Flexibility.

All of those still matter.

But, I believe another word is becoming increasingly important to the modern workplace: agency.

It is the ability to exercise meaningful influence over your working life.

It doesn’t mean that you get to do whatever you want.

It doesn’t mean that organizations don’t have objectives, customers don’t have expectations, or leaders don’t get to lead.

Agency means that within those realities, you retain some dominion over your work.

You can exercise judgment.

You can make decisions.

You can develop new skills.

You can question whether an established way of doing something still makes sense.

You can help determine how you use technology.

And, perhaps most importantly, you don’t experience your career as something that simply happens to you.

That distinction is about to become enormously important.

Agentic AI Makes Human Agency More Important, Not Less

We’ve spent much of this summer at What’s Your Work Fit? exploring the extraordinary possibilities—and some of the unintended consequences—of agentic AI.

AI can give us tremendous productive capacity.

It can remove administrative work.

It can help us research, analyze, write, organize, schedule, summarize, and solve problems faster.

But there’s another side to this transformation.

What happens when AI increasingly determines how work gets done?

What happens when an employer provides the AI tools, trains them on organizational information, integrates them into workflows, and begins expecting employees to use them every day?

What happens when your own thinking, experience, prompts, workflows, and intellectual contributions help make those systems better?

And eventually:

Who owns my workplace AI?

That’s the question we’re taking up 12PM Eastern Time (US) this Wednesday, September 2, 2026 on What’s Your Work Fit? with returning guest Nirit Cohen, work futurist, podcaster, and Forbes columnist.

Who Owns My Workplace AI? I think that lead question on this week’s Fall season premiere ultimately leads us back to agency.

Because the future of work isn’t simply about whether people use AI.

It’s about whether people remain active participants in determining how AI shapes their work.

Agency Is Something Career Professionals Can Build

We sometimes talk about agency as though an employer either gives it to you or doesn’t.

I don’t think that’s entirely true.

Career professionals can actively strengthen their own agency.

Build skills that travel with you.

Your value should never reside entirely with your current employer.

Keep developing knowledge, relationships, judgment, communication abilities, technological fluency, and professional expertise that remain valuable wherever you go next.

Understand the AI tools shaping your work.

Don’t simply become the person who knows which buttons to push.

Understand what the technology does well. Understand where it fails. Learn how to question its output. Develop your own workflows.

The career professional with agency doesn’t surrender judgment to AI.

They use AI to expand what their judgment can accomplish.

Protect your capacity to think.

This may become one of the great workplace challenges of the next several years.

Efficiency can easily become work intensification.

AI saves three hours, so another five hours of work gets dumped into your inbox.

More capacity becomes more assignments.

More speed becomes greater expectations.

Agency requires recognizing that productive capacity is not infinitely expandable human capacity.

People need time to think, learn, interact, experiment, recover, and occasionally do absolutely nothing productive.

Those aren’t defects in human performance.

They’re part of being human.

Ask more questions.

Agency requires curiosity.

Why are we doing this?

Is there a better way?

What problem are we actually solving?

What should AI do here?

What should a human do?

What happens if the AI is wrong?

Who benefits from this productivity gain?

What happens to the time we’ve saved?

Those may become some of the most valuable questions anyone can bring into a workplace.

Build a career ecosystem, not merely a résumé.

Your career shouldn’t exist entirely inside one organization.

Build professional relationships.

Participate in communities.

Share ideas.

Follow people who challenge your thinking.

Develop interests outside your employer.

Maintain relationships with people who have moved elsewhere.

And continually ask yourself:

What could I do next if this job suddenly disappeared?

That’s not disloyalty. That’s agency.

Agency Is Also About Work Fit

This is where agency and Work Fit become inseparable.

Work Fit asks a deceptively simple question:

Does the work you do fit the life you want to live?

And that answer can change.

The Work Fit you wanted at 25 may not be the Work Fit you need at 45.

The job that once gave you tremendous agency may eventually constrain it.

The workplace that once helped you grow may eventually stop doing so.

That’s why Work Fit isn’t a destination. It’s an ongoing conversation between your work and your life.

And, agency gives you the ability to participate meaningfully in that conversation.

This Wednesday: Who Owns My Workplace AI?

That’s why I’m especially excited about the Fall 2026 season premiere of What’s Your Work Fit?

This Wednesday, September 2, 2026 work futurist Nirit Cohen returns to help my co-hosts LiquidSpace CEO Mark Gilbreath and MorteVita Managing Principal Fran Saele and me explore:

Who Owns My Workplace AI?

As agentic AI becomes embedded in our daily work, the questions are getting much bigger than productivity.

Who owns the workflows we develop?

Who owns the knowledge AI learns from us?

How portable should our AI-supported capabilities become?

What happens when someone leaves an organization?

And how can career professionals maintain agency when increasingly powerful technologies sit between them and the work they do?

These aren’t abstract questions about some distant future.

They’re arriving in workplaces now.

Join the What’s Your Work Fit? Community

What’s Your Work Fit? exists to help career professionals explore questions exactly like these.

We’re building a community around a belief that work should be more than something we endure in exchange for a paycheck.

Work should be a wonderful part of the day doing other wonderful things.

And the future of work shouldn’t merely be decided for career professionals.

We should have agency in shaping it.

So, join us.

Subscribe to What’s Your Work Fit? on Substack for our continuing writing about AI, careers, leadership, the workplace, and Work Fit.

Subscribe to the What’s Your Work Fit? YouTube Channel for full conversations, recorded shows, and clips from our guests.

Follow What’s Your Work Fit? on LinkedIn to join our ongoing discussions and debates about the rapidly changing world of work.

And watch the conversation on Wednesday as Nirit helps us tackle one of the most important questions emerging from the AI workplace:

Who owns my workplace AI?

Because perhaps the even bigger question is:

How much agency will I have over the future of my work?

Our best days lie ahead.

Note: Image rendered on ChatGPT based on our prompts.

I’m Dan Smolen. As host and executive producer of What’s Your Work Fit? I help you make your work and workplace decisions result in better and more satisfying professional experiences and outcomes. I am also a Founding Member of The Future of Work Alliance.

Join What's Your Work Fit? on LinkedIn

Join What's Your Work Fit? on YouTube

A little past 2PM Eastern Time today, August 25, 2026, came the official announcement of Dolly Parton’s death.

She was a gifted singer and songwriter, the winner of 11 Grammy awards, member of both the Country Music Hall of Fame and the Rock & Roll Hall of Fame.

She acted in films like “9 to 5.”

She founded Dollywood.

Anything she set her mind to doing she did and did well.

In our present political climate in which vitriol garners the most headlines, Dolly found a way to sooth the American soul with joy and kindness.

Left, right, and center adored her.

I wasn’t a lifelong Country music fan. But, over the years, Dolly drew me in with artistry and incredible storytelling taken from her experiences growing up dirt-poor, one of 14 family members living in a one room shack in the Great Smoky Mountains of Tennessee.

I remember getting in the mail my copy of Rolling Stone featuring her and Arnold Schwarzenegger power posed in an iconic Annie Liebovitz photograph.

Who is this woman? (I wanted to know.)

When I learned of her passing, my first thought was not of the loss of the artist. I mourned the loss of the Tzadik, the righteous human who lived her life to make the world a better place.

She worked tirelessly to increase literacy, donating millions of books to children.

She donated a million dollars to help develop the Moderna Covid vaccine that kept people out of the hospital and saved lives.

She lent a hand to communities ravaged by flood and fire not for recognition, but rather because she knew it was the right thing to do.

A Tzadik.

As a podcaster I listen to episodes of great shows with not so great guests. Sometimes, I grab a not-so-great show with a phenomenal guest.

The jackpot for me is to catch a great show and a great guest.

Long before there were podcasts, Dolly joined my behind the mic avatar Terry Gross on NPR’s Fresh Air for a riveting conversation. To honor the memory of this magnificent human I hope you will take a listen to this episode broadcast in 2001.

LISTEN HERE

May Dolly Parton’s memory be for a blessing.

Our best days lie ahead.

Image credit: Annie Liebovitz/Rolling Stone (1977)

I’m Dan Smolen. As host and executive producer of What’s Your Work Fit? I help you make your work and workplace decisions result in better and more satisfying professional experiences and outcomes. I am also a Founding Member of The Future of Work Alliance.

Join What's Your Work Fit? on LinkedIn

Join What's Your Work Fit? on YouTube

Three years ago, we started by posing a simple question: What’s Your Work Fit?

Not: What do you do?

Not: Where do you work?

Not even: What do you want your next job to be?

We wanted people to think about something bigger:

Does the work you do fit who you are, how you want to live, and what you want your lasting contributions to be?

Three years later, I believe that question matters more than ever.

Because work is changing extraordinarily fast.

Should I stay?

Should I leave?

Should I become an independent professional?

How do I remain relevant as AI becomes capable of doing more of my work?

How do I build skills that will matter five years from now?

How much control should I expect over my time?

What should I expect from an employer—and what should an employer reasonably expect from me?

And perhaps most importantly:

What do I actually want from my working life?

That is why What’s Your Work Fit? has become more than a podcast.

We’re building a community that asks a lot of questions

One of the things I’ve learned from hosting hundreds of conversations about work is that the most valuable discussions don’t necessarily provide neat answers.

They help us ask better questions.

Our guests bring expertise and experience. Our co-hosts bring their perspectives. I bring mine.

But increasingly, some of the most interesting insights come from the people watching, listening, reading, commenting and challenging the premises we put forward.

That’s the community I want us to keep building.

A place for career professionals who don’t simply want somebody telling them what the future of work will be.

They want to participate in figuring it out.

And there are several ways to join us.

Subscribe to What’s Your Work Fit? on Substack

If you’re reading this here, you’re already in the right place. Our Substack gives us room to go deeper than we sometimes can during a live show.

It’s where I wrestle with the big questions emerging from our conversations about work, careers, leadership, AI, agency and Work Fit.

Lately, we’ve been asking questions such as:

If AI gives us back hours of our working week, who owns that time?

If one person supported by AI can accomplish dramatically more, who gets the productivity dividend?

If AI removes much of the administrative burden of management, could the middle manager’s job actually become more human?

And if organizations use AI simply to pile more work onto people, will some of their best talent decide that full-time employment is no longer worth it?

These aren’t predictions.

They’re invitations to think.

And to debate.

Subscribe so you can be part of those conversations.

Subscribe to the What’s Your Work Fit? YouTube Channel

Sometimes you want the entire conversation.

Sometimes you have ten minutes.

Sometimes you have two.

That’s one reason our What’s Your Work Fit? YouTube Channel has become such an important part of our experience.

You can watch our shows, return to recorded conversations and discover shorter clips focused on a particular question or insight.

It’s an easy way to build your own library of ideas about career development, workplace change, leadership and the future of work.

And when a conversation resonates, send it to somebody.

A colleague.

A friend contemplating a career move.

A manager trying to lead differently.

A young professional entering the workforce.

A person who has reached that uncomfortable point in their career when they know something needs to change, but they’re not yet sure what that something is.

Follow What’s Your Work Fit? on LinkedIn

LinkedIn is where many of our conversations continue in real time.

We post show announcements, clips, ideas from our guests, and questions designed to encourage discussion.

But the comments are often as important as the posts.

Work Fit isn’t something I can define for you. And your employer can’t define it for you, either.

You have to define it for yourself.

Hearing how other people are navigating similar questions can help enormously.

Someone at 27 will see Work Fit differently from someone at 47.

A parent may define it differently from someone without children.

An independent professional may define it differently from a corporate executive.

Someone beginning a career will have different priorities from someone contemplating the final decade of theirs.

None of them is necessarily right or wrong.

That’s the point.

Work Fit isn’t a destination

This may be the most important idea behind everything we’re doing.

You don’t discover your Work Fit at 32 and carry it around unchanged for the next 35 years.

Work Fit evolves because you evolve.

The career that fit you beautifully five years ago may not fit you today.

Your priorities change.

Your family changes.

Your financial needs change.

Your health may change.

Your ambitions change.

Technology changes what is possible and what isn’t.

And sometimes you simply wake up and realize that the things you once wanted aren’t the things you want anymore.

The challenge is giving ourselves permission—and the tools—to keep asking:

What’s my Work Fit now?

There has to be a better way to talk about work

Too much discussion about the workplace gets reduced to opposing camps.

Remote versus office.

Employer versus employee.

AI optimists versus AI pessimists.

Management versus workers.

Productivity versus quality of life.

But working lives are much more complicated than those binaries suggest.

What’s Your Work Fit? is a community where we can explore that complexity.

Sometimes we’ll agree.

Sometimes we’ll disagree.

Sometimes a guest will completely change the way we think about something.

And sometimes someone in the community will leave a comment that forces all of us to reconsider the premise.

That’s exactly what I want.

Because the future of work shouldn’t be a conversation conducted only by CEOs, economists, consultants, technology companies and people sitting on conference stages.

The people actually doing the work deserve a voice in defining what comes next.

So, join us.

Subscribe to What’s Your Work Fit? on Substack.

Subscribe to the What’s Your Work Fit? YouTube Channel.

Follow What’s Your Work Fit? on LinkedIn.

Watch the live shows.

Catch the recorded conversations when your schedule allows.

Watch and share the clips.

Read the stories.

Challenge an argument.

Leave a comment.

Ask us a question that we haven’t thought to ask.

And bring someone else into the conversation.

Because the goal isn’t for me to tell you what your Work Fit should be.

Our mission is to create a community, conversations and resources that help you figure it out for yourself—and keep figuring it out as your life and career evolve.

So I’ll leave you with the question that started all of this:

What’s Your Work Fit?

I’d love to hear your answer.

Our best days lie ahead.

Note: image rendered in ChatGPT based on our prompts.

I’m Dan Smolen. As host and executive producer of What’s Your Work Fit? I help you make your work and workplace decisions result in better and more satisfying professional experiences and outcomes. I am also a Founding Member of The Future of Work Alliance.

Join What's Your Work Fit? on LinkedIn

Join What's Your Work Fit? on YouTube

There is an implicit understanding about what we may carry from one job to the next.

We don’t take the company’s databases. We don’t take proprietary information. We don’t take intellectual property that belongs to our employer.

We do, however, take something enormously valuable: we take what we learned.

Our training. Our experience. Our judgment. Our professional instincts. Our understanding of what works and what doesn’t. The relationships we’ve built. The mistakes we’ve made and learned from.

All of that becomes part of our professional portfolio.

And when we move to the next opportunity, it moves with us.

But now agentic AI is beginning to complicate that bargain.

And it raises a question I don’t think we’ve spent nearly enough time considering:

Who owns my workplace AI?

That’s the question we’ll tackle when work futurist, podcaster, and Forbes columnist Nirit Cohen returns to What’s Your Work Fit? for the premiere of our Fall 2026 season.

And the more I think about it, the bigger the question becomes.

AI isn’t just a tool anymore

Agentic AI increasingly learns how we work. It can understand our preferences, organize information around our needs, anticipate tasks, remember context, interact with other systems and help execute increasingly complicated workflows.

Over time, the AI supporting me could become very different from the AI supporting you. Because I shape it.

Imagine spending three years working with an AI agent.

You teach it how you prepare for meetings.

It learns the questions you ask before making an important decision.

It understands how you research competitors.

It knows how you structure a presentation.

It recognizes the information you consider important and the information you usually disregard.

It becomes increasingly useful because it has been informed by thousands of interactions with you.

Then one Friday afternoon, you leave the company.

What happens Monday morning?

Do I leave part of myself behind?

This is where the issue gets fascinating.

That begins to blur some boundaries we’ve understood for generations.

When I leave an employer, the company doesn’t get to erase my memory.

It cannot reclaim the judgment I developed while working there.

It cannot tell me that the management skills I developed belong to the organization.

Those capabilities became a part of me.

But AI potentially externalizes some of that learning.

Instead of all of my accumulated professional intelligence residing inside my head, some of it may increasingly reside inside systems owned by my employer.

And that creates a very different employment relationship.

Your next résumé may not tell the whole story

For decades, career mobility has been built around credentials and experience.

We tell prospective employers:

Here’s what I’ve done.

Here’s what I’ve learned.

Here’s what I know how to do.

What happens when our productive capability becomes increasingly intertwined with AI?

Perhaps the more interesting future question isn’t:

What can you do?

It is:

What can you and your AI do together?

That could fundamentally change how we think about professional portfolios.

Today, someone might arrive at a new company with a laptop, a smartphone, a résumé, and years of experience.

Tomorrow, might they expect to arrive with a personalized AI environment that contains their preferred workflows, prompts, decision frameworks and ways of organizing work?

In other words:

Could our AI eventually become part of our professional identity?

And if so, should some portion of it be portable?

This could become a new source of workplace power

There is another dimension here that deserves attention.

AI portability could become an issue of worker agency.

Companies have historically controlled many of the resources people need to be productive: the office, the equipment, the databases, the software and the institutional knowledge.

Cloud computing and distributed work loosened some of those boundaries.

Agentic AI could redraw them again.

Imagine telling a talented professional:

“You are free to leave. But the AI system you’ve spent four years teaching how you work stays here.”

That might feel very different from handing in a company laptop.

Companies have a legitimate side of this argument, too

This isn’t simply an employee-rights question.

Employers will have powerful reasons to protect their AI environments.

An agent trained inside a pharmaceutical company, financial institution, consulting firm or technology company may contain enormous amounts of proprietary knowledge.

Organizations cannot simply allow that intelligence to walk out the door.

And if the company paid for the AI, provided the data, and compensated the employee while the system was being developed, leadership may reasonably ask:

Why wouldn’t the resulting capability belong to us?

That’s precisely why this discussion is going to get complicated.

Because both sides may have legitimate claims.

Perhaps we need to separate company intelligence from human intelligence

One possible answer is that we eventually develop a clearer distinction between organizational AI assets and personal professional AI assets.

Company information stays with the company.

Customer data stays with the company.

Proprietary processes stay with the company.

But perhaps some things become portable:

Your preferred ways of organizing information.

Your personal prompt libraries.

Your communication preferences.

Your decision frameworks.

Your learning history.

Your professional development record.

Your nonproprietary workflows.

Maybe someday leaving a company will include something resembling an AI portability process.

The organization keeps what belongs to it.

The worker takes what legitimately belongs to them.

That sounds technologically complicated.

Legally complicated.

And culturally complicated.

Which is exactly why we should start discussing it before the technology makes the decision for us.

This is ultimately a Work Fit question

I’ve spent a lot of this year thinking and writing about what happens when AI gives people dramatically greater productive capacity.

Because Work Fit is fundamentally about agency.

It asks whether our work fits who we are, how we want to live and what we hope to contribute.

Increasingly, the AI we work alongside may help determine the answer.

If AI becomes an extension of our professional capability, we need to decide whether it is simply another corporate asset—or whether some part of what we build with it becomes part of the professional selves we carry through our careers.

And I suspect the answer won’t be simple.

Which is why I want to hear from you.

If you spent years shaping an AI agent around the way you work, should you be allowed to take some version of it to your next job?

Where should we draw the line between an employer’s intellectual property and an employee’s accumulated professional intelligence?

Should personalized AI eventually become as portable as a résumé, professional certification or LinkedIn profile?

And perhaps the biggest question:

If your AI becomes part of how you think and work, who owns it—you or your employer?

Let’s ask Nirit

The Fall 2026 season of What’s Your Work Fit? premieres Wednesday, September 2.

Our returning guest, work futurist, podcaster and Forbes columnist Nirit Cohen, will join me to tackle Who Owns My Workplace AI?

Because this episode will be prerecorded, I’m gathering questions in advance.

So send me yours.

What should we ask Nirit?

What concerns you about the AI you’re beginning to use at work?

And what rights—or responsibilities—do you believe workers should have over the AI capabilities they help create?

The workplace is changing faster than most of the rules governing it.

We shouldn’t wait for those rules to be written for us.

Please post your questions in the comment thread of this Substack.

Our best days lie ahead.

Note: image rendered based on our prompts and photo of Nirit Cohen.

I’m Dan Smolen. As host and executive producer of What’s Your Work Fit? I help you make your work and workplace decisions result in better and more satisfying professional experiences and outcomes. I am also a Founding Member of The Future of Work Alliance.

Join What's Your Work Fit? on LinkedIn

Join What's Your Work Fit? on YouTube

What’s Your Work Fit?’s new season is about to drop. And we could not be more excited.

Our new fall 2026 season explores inconvenient truths, such as how agentic AI may not actually provide career professionals the agency and “Work Fit” that they hope to achieve.

We’re at a pivotal moment in the work we do and the modern workplace where it all happens.

Agentic AI has given us the means to do more—and more varied—work which may expand both workplace productivity and the productive capacity of people.

Will we rise to channel AI, to make the work that we do a wonderful part of the day doing other wonderful things (a Work Fit)?

Or, will workers engaged with it find themselves compressing more output into a perpetually longer day, rendering themselves deeply disengaged from the workplace?

On the What’s Your Work Fit? season premiere, which airs at 12PM Eastern Time (US) on Wednesday, September 2, 2026, our returning guest, work futurist, podcaster, and Forbes columnist Nirit Cohen, will explore with us this hot workplace topic: Who Owns My Workplace AI?

We’re being forced to reckon with new workplace realities.

In real time, What’s Your Work Fit? is responding to it all by asking world recognized future of work and workplace experts probing questions to expand our knowledge, but also to help us make our days more enjoyable and fulfilling.

Our ask: Become Part of the What’s Your Work Fit? Community.

Subscribe to us on the What’s Your Work Fit? Channel on YouTube, and on LinkedIn, and here on Substack.

Post your questions and comments in our feeds. And make your voice heard.

And please, if you like the experience, tell five of your friends about What’s Your Work Fit?

That’s how we’re building a from-the-ground-up community that’s purposed to make the work we do a wonderful part of the day. A Work Fit.

And when that happens our best days lie ahead.

Image credit: Dan Smolen, August 22, 2026.

I’m Dan Smolen. As host and executive producer of What’s Your Work Fit? I help you make your work and workplace decisions result in better and more satisfying professional experiences and outcomes. I am also a Founding Member of The Future of Work Alliance.

Join What's Your Work Fit? on LinkedIn

Join What's Your Work Fit? on YouTube

This week, Substacker and host of the Humanity Working podcast, Paul Slater, responded to a question we posed about what Agentic AI might do to the value of human capacity in the modern workplace.

His answer deserves more than a passing mention.

“[Organizations have changed] a lot in 100 years, and the ones that survive and retain top talent will need to change again. I believe they will need a combination of super-thinkers (providing a wisdom layer over all the work being done), super-doers (building teams of agents that accomplish 100x of the work of traditional employees) and super-prioritizers (deciding what should be done and what should not be.”

He went on:

“I suspect traditional org structures will go, and instead groups of people plus AI will swarm around problems for periods of hours to months.”

That is a provocative prediction. But perhaps the most interesting part is what it implies about the value of being human at work.

For decades, organizations have been built around a relatively simple proposition: divide work into functions, put people into roles, give them managers, and coordinate their efforts through an increasingly elaborate hierarchy.

The assumption underneath all of this was that human attention and execution were scarce.

Agentic AI attacks that assumption directly.

From employees to capability systems

Paul’s distinction between super-thinkers, super-doers and super-prioritizers is useful because it points toward three different forms of human advantage.

Super-thinkers provide the wisdom layer.

They don’t necessarily produce the most work. In fact, they may produce considerably less output than the agents around them.

Their value comes from knowing which questions are worth asking, recognizing patterns that aren’t obvious in the data, understanding context, challenging assumptions and making judgments where there is no clean answer.

An AI can generate ten thousand possibilities. The super-thinker knows which three are worth taking seriously.

Super-doers are different.

They are the people who understand how to turn intention into an operating system of agents. They know how to decompose a problem, orchestrate AI systems, build workflows, evaluate outputs and keep improving the machinery.

Their leverage could be extraordinary.

One person with a well-designed agentic system might eventually accomplish work that previously required an entire team. But that doesn’t necessarily mean the person is simply a more productive version of today’s employee.

They are something closer to a manager of machine capability.

And then there are super-prioritizers.

This may be the rarest and most important category of all.

When the cost of doing things falls dramatically, deciding what not to do becomes more valuable.

An organization awash in cheap execution can easily become an organization awash in pointless execution.

If agents can launch ten experiments before lunch, who decides which experiments deserve to exist?

If AI can produce a hundred strategies, who decides which problem the company should actually solve?

If almost anything becomes technically possible, how do we decide what is desirable?

The constraint moves from execution to judgment.

The great unbundling of the job

For most of the industrial and information ages, a “job” bundled together many different kinds of activity.

A product manager researched markets, wrote documents, coordinated people, analyzed data, attended meetings and made decisions.

A lawyer researched precedent, drafted documents, reviewed work and advised clients.

A marketer analyzed audiences, created campaigns, wrote copy and measured results.

A software engineer wrote code, debugged it, documented it and collaborated with colleagues.

Agentic AI begins to unbundle these activities.

Some will become almost entirely automated.

Some will become dramatically faster.

Some will become more valuable precisely because humans remain responsible for them.

And some activities that currently occupy enormous amounts of organizational time—status reporting, coordination, information retrieval, routine analysis—may simply disappear.

That raises a difficult question:

If the work changes faster than the job titles do, why would we keep organizing people around jobs?

Paul’s vision of people and AI “swarming” around problems points toward a radically different model.

Imagine a company where there is no permanent twelve-person team for a particular initiative.

Instead, a problem emerges.

A small group of humans defines it.

Agents are assembled around it.

Specialists join for the portions requiring their expertise.

The group works intensely for two weeks, or two months.

The problem is solved—or abandoned.

The human participants move on.

Then, the agents reconfigure for the next challenge.

That sounds less like a traditional organization and more like a dynamic network of human judgment and machine execution.

And it could have profound implications for Work Fit.

Because if people are no longer tethered quite so tightly to fixed roles, fixed teams and fixed methods of execution, they may gain something many workers have wanted for decades: greater agency over how they contribute, what problems they solve, how they develop, and perhaps even how they organize their time.

The unbundling of the job could therefore become the unbundling of some of the constraints that have made work fit poorly into human lives.

But only if leaders choose to make it so.

But there is a catch

The obvious danger is that we mistake capacity for value.

If AI gives everyone 100x more execution capacity, it does not follow that everyone will create 100x more value.

In fact, the opposite could happen.

We could end up with organizations where everyone is extraordinarily productive at producing things nobody wants.

More analysis.

More content.

More software.

More experiments.

More meetings summarized by agents.

More dashboards.

More strategies.

More everything.

The bottleneck becomes meaning. And, this is why task-prioritization matters so much.

A world of abundant execution makes taste economically important.

So does courage.

So does the ability to say no.

So does the ability to hold a coherent view of the future when the evidence is incomplete.

And so does wisdom—which is a rather awkward thing for organizations to measure, recruit for, or put into a performance review.

There is another danger here, too.

Organizations could take every minute of capacity that AI creates and immediately refill it with more work.

More assignments. More goals. More metrics. More deliverables.

If AI allows someone to accomplish by Wednesday what once took until Friday, the organization could simply decide that Thursday and Friday are now available for still more production.

That would be one possible AI future. But it would hardly be a Work Fit future.

The alternative is to recognize productive capacity as something to be stewarded rather than endlessly consumed.

Some of the dividend created by AI could go toward better work: learning, experimentation, collaboration, reflection, mentoring, creativity—and greater human agency over the day.

That choice may become one of the defining leadership questions of the AI era.

The death of the manager—or the birth of a different one?

There is another implication worth debating.

If agents can coordinate much of the work currently performed by middle management, traditional managerial structures could come under enormous pressure.

But that doesn’t necessarily mean management disappears.

It may mean that management becomes more human.

Today, managers often spend substantial amounts of time coordinating information and activity: allocating resources, checking progress, preparing reports, chasing updates and making sure processes are followed.

Agents will increasingly be able to do much of this.

What remains?

Coaching.

Conflict resolution.

Judgment.

Context.

Motivation.

Culture.

Development.

Helping people figure out where they can make their greatest contribution.

Deciding when the process itself is wrong.

Thus, the parts of management that were always difficult to automate may become the main event.

And this is where management and Work Fit may increasingly intersect.

A good manager in an AI-supported organization may be judged less by how effectively they extract productivity from people and more by how effectively they expand their people’s capacity to do consequential work without consuming their lives in the process.

The irony is that AI could make good management more important while making much of today’s management work obsolete.

And what happens to the employee value proposition?

This may be the most uncomfortable question.

If an entrepreneur can assemble a handful of exceptional people and a vast amount of AI capability, why would talented people want to work for a large organization?

For much of the last century, large organizations offered things individuals could not easily reproduce themselves:

capital

technology

distribution

information

colleagues

training

reputation

stability

career progression

AI is beginning to weaken some of those advantages.

A talented individual with access to powerful models, agents and cloud infrastructure can potentially build an astonishing amount with a very small team.

So companies may increasingly have to answer a different question:

Why should an exceptional person work here rather than build something themselves?

And this is where Work Fit could move from being perceived as an employee preference to becoming a genuine source of competitive advantage.

Talented people may increasingly ask:

Will I have meaningful agency here?

Will I learn?

Will I work on problems worthy of my time?

Will I be surrounded by people who make me better?

Will this organization use AI to eliminate pointless work—or merely use it to give me more work?

Will I have some dominion over my day?

Can I do exceptional work here and still have a life that belongs to me?

Those aren’t peripheral questions about workplace culture.

In a world where highly capable people have increasingly credible alternatives to traditional employment, they may become fundamental questions of talent strategy.

That makes Paul’s final point particularly important: organizations will need to become “exciting places to work.”

Not merely comfortable places.

Not places with good perks.

Places where ambitious people can accomplish things that would be difficult to accomplish alone.

The best companies may compete less on employment benefits and more on the density of opportunity—and the quality of Work Fit.

The ability to work on consequential problems.

Access to exceptional peers.

A culture that rewards judgment.

The freedom to experiment.

The resources to turn an idea into reality.

Enough flexibility for work to fit into a meaningful life rather than requiring life to fit around work.

And perhaps above all, a sense that being part of the organization creates more agency, not less.

Adaptability becomes an organizational capability

Paul identifies adaptability and resilience as the characteristics that will matter most.

That feels right—but it also raises a paradox.

Traditional organizations are often designed to minimize change.

They create roles, processes, reporting lines and budgets precisely so that thousands of people don’t have to renegotiate how the company works every morning.

Agentic AI may make the environment change so rapidly that the ability to repeatedly redesign the organization becomes itself a competitive advantage.

The organization of the future may therefore be less like a machine and more like an ecosystem.

Stable enough to provide identity and purpose.

Fluid enough to reorganize itself around emerging problems.

Small enough, perhaps, that bureaucracy doesn’t become the dominant operating system.

Intelligent enough to know when the structure that worked yesterday has become an obstacle today.

And human enough to recognize that adaptability cannot simply mean demanding that people endlessly absorb more change, more work and more pressure.

A truly adaptive organization should be capable of changing itself around human beings, too.

The uncomfortable possibility

There is a darker interpretation of all this.

Perhaps the future doesn’t consist of “super-thinkers, super-doers and super-prioritizers” working alongside AI.

Perhaps it consists of a very small number of humans with extraordinary leverage—and a much larger number of people whose economic value is dramatically reduced.

If one person can orchestrate 100 agents, what happens to the 100 people who previously performed the underlying work?

We should not assume that technological productivity automatically translates into broadly shared prosperity.

Nor should we assume that technological productivity automatically translates into better work.

Those are choices.

If all of the AI dividend flows toward shareholders, executive compensation and ever-greater expectations for human output, then we may create extraordinarily efficient organizations populated by increasingly exhausted and disengaged people.

But there is another possibility.

Perhaps lower execution costs allow us to attempt vastly more things, creating new categories of work and opportunity that are difficult to imagine today.

Perhaps organizations can become smaller, more fluid and more human.

Perhaps people can spend less of their lives performing administrative debris and more of them exercising judgment, curiosity, creativity and discernment.

Perhaps the same technology capable of dramatically increasing productive capacity can also help millions of people achieve something that has remained elusive:

work that fits who they are, how they want to live, and the contribution they hope to make.

Both futures are plausible.

Technology alone won’t decide which one we get.

Leaders will.

So what should leaders be asking?

Not simply:

“How can we use AI to make our employees more productive?”

That question assumes the existing organization is basically correct—and that the purpose of AI is to extract still more output from the people already inside it.

A more interesting set of questions might be:

What would our organization look like if execution were almost free?

Which human judgments would become more valuable?

Which roles exist primarily because information and coordination are currently expensive?

What would we stop doing if we could?

How quickly could we form and dissolve teams around problems?

What should we do with the human capacity that AI gives back to us?

How much of the AI dividend should become better business performance—and how much should become better work?

How could AI give our people greater agency over their work and their time?

What would make a brilliant person choose to join us rather than build alone with AI?

And perhaps the biggest question:

What is an organization for when a single human can command an extraordinary amount of machine capability?

Maybe the answer cannot simply be to produce more.

Maybe part of the answer must be to create a place where humans can accomplish things they could not accomplish alone while achieving a Work Fit that makes the rest of their lives richer rather than poorer.

We don’t know the answer yet.

But, honestly, we are about to find out.

Over to you

Do you agree with Paul’s three-part model of super-thinkers, super-doers and super-prioritizers?

Will traditional org charts really give way to temporary human-AI “swarms”—or will hierarchy prove much more resilient than the AI optimists expect?

If AI yields extraordinary new productive capacity, who should benefit from that dividend?

And, if highly capable people can increasingly build outside traditional organizations, will Work Fit—the promise of agency, meaningful work, development and dominion over one’s time—become one of the most important ways companies compete for talent?

Or will the pressure for still more productivity overwhelm that possibility?

I’d love to hear arguments on both sides.

Our best days lie ahead.

Note: image in this post rendered on ChatGPT based on our prompts.

I’m Dan Smolen. As host and executive producer of What’s Your Work Fit? I help you make your work and workplace decisions result in better and more satisfying professional experiences and outcomes. I am also a Founding Member of The Future of Work Alliance.

Join What's Your Work Fit? on LinkedIn

Join What's Your Work Fit? on YouTube

We keep asking whether AI will take our jobs.

Maybe we’re asking the wrong question.

What if it doesn’t cause millions of people to lose their jobs?

What if AI causes millions of people to decide they no longer want the jobs companies are offering them?

That possibility deserves much more attention.

A workplace with more bots.

Fewer employees.

Far fewer middle managers.

And a growing population of talented people working around the edges of companies as consultants, fractional executives, specialists, creators and independent contractors.

The 1099 economy may be about to get a boost.

We’ve Seen This Movie Before

Every major technological advancement in the workplace has changed not only how work gets done, but also the pace at which humans are expected to do it.

I saw this repeatedly during my own career in advertising and marketing.

And, I remembered what went into producing a major marketing campaign back in the day.

Humans had to envision the campaign.

Sell it internally.

Write it.

Design it.

Program it.

Produce it.

Print it.

Distribute it.

Manage its execution.

And then, after everything was finished, analyze its performance.

There were thousands of individual decision points along the way.

And all of that required something that has become increasingly scarce in modern business: time.

A campaign might require eight or ten weeks from conception to execution.

Technology gradually compressed that calendar.

And agentic AI could crush it.

Work that required eight weeks might soon require three.

Work that required three weeks might require three days.

That sounds wonderful.

Until someone asks the inevitable question:

If we can do three campaigns in the time it once took to do one, why don’t we?

Welcome to the AI Productivity Trap

This is where the conversation about AI productivity becomes much more complicated.

The assumption behind much of the corporate enthusiasm surrounding agentic AI is that greater productive capacity is inherently good.

And economically, that’s understandable.

If an organization can produce more with the same number of people, productivity rises.

If it can produce more with fewer people, margins may improve.

If a marketing department can launch 25 campaigns instead of 15, why wouldn’t leadership want 25?

Productive capacity isn’t the same thing as human capacity.

The machine may be able to keep going.

The human being still has limits.

And when technology compresses the time required to complete work, organizations have historically had a tendency to fill the newly available space with—you guessed it—more work.

AI could turn that tendency into an organizational philosophy.

Your inbox clears faster?

Here’s more.

Your AI agent completed the analysis?

Here’s another project.

The quarterly plan is ahead of schedule?

Let’s increase the target.

Efficiency becomes acceleration.

Acceleration becomes expectation.

And expectation eventually becomes exhaustion.

More on the Plate Means More That Can Go Terribly Wrong

There’s another problem with continually increasing the volume of work.

Complex work contains decision points.

And every additional decision point creates another opportunity for something to go wrong.

The fact that AI can generate a marketing plan, presentation, financial model, customer analysis or communications strategy extraordinarily quickly doesn’t eliminate the need for human judgment.

In some ways, it increases it.

Someone still has to ask:

Is this right?

Does this make sense?

What are we missing?

Should we actually do this?

What happens if we’re wrong?

Who gets hurt?

What unintended consequence haven’t we considered?

The faster work moves, the easier it becomes to confuse velocity with progress.

And eventually the humans responsible for making all those judgments may discover that they have become the bottleneck in a system specifically designed to eliminate bottlenecks.

Clearly, that is not Work Fit.

Flame Out. Burn Out. Rust Out.

I’ve been thinking about three different ways talented people can respond to this environment.

They can flame out—the workload becomes overwhelming and something breaks.

They can burn out—sustained pressure gradually depletes their energy, enthusiasm and ability to perform.

Or they can rust out—their work becomes so automated, transactional and disconnected from meaning that they simply stop caring.

We spend a lot of time discussing burnout. Rust out may prove just as important in the AI workplace.

Imagine being highly capable, well educated and ambitious, only to discover that your role increasingly consists of supervising automated workflows, responding to an endless stream of AI-generated output and racing from one accelerated deadline to another.

You aren’t necessarily working 70 hours a week. You just don’t feel particularly human while you’re working.

How long before talented people begin asking:

Why am I doing this?

And then:

Why am I doing this for you?

The Best Talent May Have Another Option

This is where the employment implications become fascinating.

For decades, large corporations possessed enormous advantages in attracting talent.

They offered salaries.

Benefits.

Career ladders.

Training.

Prestige.

Professional networks.

Security—or at least the perception of it.

And they spent fortunes on employer branding.

Come work here.

Build your career here.

Become part of our culture.

Bring your whole self to work.

We’re an amazing place to work.

But what happens when the lived experience inside the organization no longer resembles the brand?

What happens when there are fewer people?

Fewer managers?

Smaller teams?

More automation?

More work?

More monitoring?

More pressure?

And fewer opportunities to form the human relationships that once made organizational life meaningful?

The beautifully designed corporate campus may still be there.

The espresso machine may still be there.

The employer-branding campaign may still tell us how extraordinary the culture is.

But the humans inside the building may feel increasingly lonely.

The Great Detachment

We talk frequently about employee engagement. Maybe we should start talking about employee detachment.

An organization can become extremely efficient while simultaneously becoming a terrible community.

Those two outcomes are not mutually exclusive.

Imagine a Fortune 500 company in 2030 that produces substantially more revenue per employee than it did in 2025.

Wall Street might celebrate that.

But suppose the organization employs 30 percent fewer people.

Management layers have been dramatically reduced.

AI agents coordinate much of the routine work.

Teams are smaller.

People move quickly from project to project.

And the remaining employees spend much of their day interacting with machines rather than colleagues.

On a spreadsheet, the company may look extraordinary.

From inside the organization, it may feel strangely empty.

That creates another unintended consequence: talented career professionals with the greatest ability to leave may be the first ones who do.

What If the Best People Choose the Edges?

The most capable workers increasingly have another choice.

They can leave.

Not necessarily for another full-time job.

They can become independent.

A marketing strategist might work with four companies.

A finance executive might become a fractional CFO.

A communications leader might build a portfolio of clients.

A software engineer might assemble project work across several organizations.

A researcher, designer, recruiter, analyst or operations executive might decide that selling expertise is preferable to selling control over the day.

AI itself could make that transition easier.

Independent professionals historically needed organizations because organizations provided infrastructure.

Research.

Administrative support.

Technology.

Marketing.

Scheduling.

Billing.

Analysis.

Production capabilities.

AI can provide an increasing amount of that infrastructure to a single person.

Suddenly, a highly capable independent professional can possess productive capabilities that once required a small firm.

And that changes the employment bargain.

The New Career Goal May Be Dominion Over the Day

For much of the industrial and post-industrial era, professional success was associated with organizational ascent.

Bigger title.

Bigger office.

Bigger team.

Bigger budget.

Higher floor.

Maybe the next era looks different.

One of the most desirable markers of professional success becomes this: I control my day.

I decide when I work.

I decide whom I work with.

I decide which projects deserve my attention.

I decide how much work is enough.

I decide when I stop.

That’s agency. That’s also Work Fit.

And if full-time employment increasingly means allowing an AI-accelerated organization to determine how every newly created hour of productive capacity gets filled, independent work may become extraordinarily attractive.

Especially to experienced people who possess skills the market values.

So, What Happens to Middle Management?

This scenario also raises an uncomfortable question about managers.

Organizations already have enormous incentives to reduce management layers.

Agentic AI makes that easier.

Status reports can be automated.

Scheduling can be automated.

Workflows can be coordinated automatically.

Performance dashboards can update continuously.

Information can travel through organizations without requiring as many humans to gather, summarize and transmit it, thus, the organization becomes flatter.

That could be good.

But what happens if we eliminate not only bureaucratic management, but also the humans who performed the deeply human parts of management?

Who coaches?

Who notices when someone is struggling?

Who resolves conflict?

Who helps a young employee understand an ambiguous situation?

Who recognizes potential?

Who tells someone, “You’re ready for something bigger”?

Who creates belonging?

AI may remove many of the administrative reasons we needed middle managers.

But that doesn’t mean it eliminates the human reasons we need them.

And if companies aren’t careful, they may discover that they eliminated both at once.

The Lean Company Could Become a Lonely Company

This may be one of the great paradoxes of the agentic AI workplace.

Companies could become:

More productive.

More profitable.

More responsive.

More technologically capable.

And, profoundly less human.

That doesn’t necessarily mean people will revolt. They may simply leave.

Quietly.

The strongest talent may increasingly migrate toward independent work, portfolio careers, fractional leadership and small entrepreneurial ventures.

The large enterprise remains.

Instead of employing everyone it needs, the large enterprise may become something closer to a platform surrounded by talent.

A relatively small core of employees.

A sophisticated layer of AI agents.

And a large orbit of independent humans brought in when judgment, creativity, expertise, relationships or leadership are required.

The boundary of the company starts becoming porous.

Who “works there” becomes surprisingly difficult to answer.

Maybe We’re Debating the Wrong Future

The dominant debate about AI and employment remains:

How many jobs will AI eliminate?

I wonder if that’s too simplistic.

Maybe the more interesting question is:

What kind of employment system will AI create?

Perhaps the future isn’t mass unemployment but rather mass reorganization of employment.

Companies become leaner.

Management layers shrink.

AI agents perform enormous amounts of organizational work.

Some of the most capable human talent migrates from the center of organizations toward their edges.

Not because they couldn’t get a job.

Because they decided the job wasn’t worth the loss of agency.

And here’s the question I want us to debate:

Could companies become so good at using AI to increase productivity that they accidentally make full-time employment less attractive?

The relentless compression of time may cause organizations to keep piling more work onto fewer people?

Employer engagement may actually decline further as companies become leaner and more automated?

The elimination of middle management may also eliminate some of the relationships that make workplaces feel human?

I don’t know. None of us do.

But I think we need to consider the possibility that the defining employment story of the AI era won’t simply be companies replacing humans with machines.

It could be something much stranger:

Companies may choose more machines and fewer people—while some of their best people leave for a future without the employment structure that companies provide.

What do you think? Is this where we’re headed?

Or are we underestimating the ability of organizations to use AI to make human work—and full-time employment—better?

Despite the unintended consequences before us, I still believe that our best days lie ahead.

Note: image in this post rendered on ChatGPT based on our prompts.

I’m Dan Smolen. As host and executive producer of What’s Your Work Fit? I help you make your work and workplace decisions result in better and more satisfying professional experiences and outcomes. I am also a Founding Member of The Future of Work Alliance.

Join What's Your Work Fit? on LinkedIn

Join What's Your Work Fit? on YouTube

Since workers left farm and field for factories and offices, time and productivity have been bound together.

You showed up. You worked your hours. You went home.

The 40-hour workweek became more than a scheduling convention. It became the basic unit through which employers purchased human productive capacity.

Five days.

Eight hours a day.

Forty hours.

The arrangement made a certain amount of sense in an industrial economy. If you were assembling automobiles, processing insurance claims, typing correspondence, or managing paper records, output was closely connected to the number of human hours available to produce it.

But what happens when that connection begins to break?

Agentic AI may be about to force that question upon us.

Because if I can accomplish by Wednesday afternoon what once took me until Friday at 5 p.m., an uncomfortable question follows:

Why does my employer automatically own Thursday and Friday?

The 40-hour week was built for another economy

We tend to talk about the 40-hour workweek as though it were a law of nature.

It isn’t.

It is a negotiated artifact of industrialization.

The great labor battles of the 19th and early 20th centuries were, in large measure, battles over time. Workers fought against brutally long days and weeks. The eight-hour movement eventually helped establish a new social bargain: employers could purchase a substantial portion of a worker’s productive time, but not all of it.

That was progress.

And eventually the 40-hour week became deeply embedded in American economic life.

But notice what remained largely unquestioned.

We continued to measure work in hours.

Even as manufacturing gave way to services, and services gave way to knowledge work, we kept the industrial clock.

Accountants worked 40 hours.

Marketers worked 40 hours.

Software developers worked 40 hours.

Human resources professionals worked 40 hours.

Managers worked 40 hours—and often many more.

The nature of the output changed dramatically.

The unit used to measure the worker’s contribution did not.

And then came AI

For years, technology improved workplace productivity incrementally.

Email accelerated correspondence.

Spreadsheets accelerated calculation.

Search engines accelerated research.

Collaboration platforms accelerated communication.

But humans remained firmly in the workflow.

Agentic AI potentially changes the equation because it doesn’t merely help us perform individual tasks faster. Increasingly, it can perform sequences of tasks on our behalf.

Research.

Analysis.

Scheduling.

Summarization.

Drafting.

Reporting.

Data retrieval.

Presentation development.

Workflow management.

And increasingly, coordination among other AI agents.

That means the productive capacity of an individual knowledge worker could expand substantially without a corresponding increase in human working time.

This is where the 40-hour workweek begins to become difficult to defend.

Imagine that a marketing manager in 2024 required five days to complete a week’s responsibilities.

By 2027, aided by sophisticated AI agents, she can accomplish comparable work by Wednesday afternoon.

What happens next?

There are at least two very different possibilities.

Possibility One: The company takes the dividend

This is probably the outcome many workers already expect.

You finished your old workload in three days?

Wonderful.

Here’s more work.

The productivity gains created by AI simply raise expectations.

Instead of managing five accounts, manage eight.

Instead of producing two reports, produce six.

Instead of overseeing one project, oversee three.

Instead of responding to 50 customers, respond to 150.

The workweek remains 40 hours—or 45 or 50—but considerably more productive activity is squeezed into it.

From the perspective of a quarterly earnings report, the logic is seductive.

Shareholders benefit.

Margins improve.

Executives celebrate productivity gains.

But the human being may experience something very different.

AI was supposed to remove drudgery.

Instead, it becomes the mechanism through which more drudgery can be assigned.

We should be very careful about calling that progress.

Possibility Two: The worker shares in the dividend

There is another possibility.

Perhaps the employee who once needed 40 hours to produce a certain outcome now needs 32.

Or 30.

Or 25.

And perhaps we stop treating the remaining hours as automatically belonging to the employer.

That doesn’t necessarily mean everyone heads to the golf course at noon on Wednesday.

It could mean more time for learning.

More time for mentoring younger colleagues.

More time for experimentation.

More time for creative thinking.

More time for family.

More time for community.

More time for exercise and health.

Or, sometimes, simply more time that belongs to you.

That last possibility may be the most radical.

Are employers buying time or outcomes?

This may become one of the defining workplace questions of the next decade.

What exactly is an employer purchasing when it hires a knowledge worker?

Forty hours?

Or valuable outcomes?

We have often blurred the two because, historically, they were difficult to separate.

If a project required 40 hours of human effort, then purchasing 40 hours and purchasing the outcome were essentially the same thing.

But if AI helps someone produce that outcome in 25 hours, they become very different propositions.

And that creates an extraordinary tension.

The employer may say:

We pay you for 40 hours. If you finish one assignment early, we’ll give you another.

The employee may reasonably respond:

But if my experience, judgment, and effective use of AI allow me to produce twice as much value in the same amount of time, shouldn’t I participate in that productivity gain?

Both sides can make an argument.

Which is precisely why we need to start having this conversation now.

Public companies may face the greatest temptation

I am particularly interested in what happens inside publicly traded companies.

The pressure to capture the AI productivity dividend will be enormous.

Imagine telling investors that your company has deployed AI systems that increase employee productive capacity by 30 percent.

What happens next?

Does the company announce that employees can now work four-day weeks?

Perhaps.

But Wall Street may ask another question:

Why aren’t you using that additional capacity to grow earnings?

That pressure matters.

Corporate leaders who genuinely want to create better working lives may find themselves caught between two competing definitions of success.

One says AI should allow humans to work better.

The other says AI should allow humans to produce more.

Those are not necessarily the same thing.

And the difference between them may shape the workplace for a generation.

Productivity cannot be the only measure

There is another problem with simply filling every hour AI frees.

Human beings are not machines with unused processing capacity.

We need cognitive white space.

We need conversations that don’t have immediate deliverables attached to them.

We need time to think.

We need time to notice things.

We need time to build relationships.

We need time to develop younger colleagues.

We need time to question whether what we’re doing makes sense at all.

Ironically, the companies that immediately stuff every AI-created opening with additional work may destroy some of the very human capabilities they will need most in an AI-rich economy.

Judgment.

Discernment.

Curiosity.

Creativity.

Empathy.

Mentorship.

Strategic thinking.

Those capabilities do not necessarily flourish when every minute of the day is optimized for measurable output.

This is ultimately a Work Fit question

For me, this discussion leads directly back to Work Fit.

Work Fit isn’t about avoiding hard work.

And it certainly isn’t about expecting employers to pay people for doing nothing.

It is about asking whether the design of work still makes sense for the lives people are trying to live and the outcomes organizations are trying to achieve.

For more than a century, 40 hours became our default answer.

But perhaps it was never the answer.

Perhaps it was simply the best compromise available at the time.

Technology has changed.

Work has changed.

Productivity has changed.

And now AI may dramatically change the amount of human time required to create economic value.

If that happens, our assumptions about the workweek should change with it.

Maybe the future is a four-day week.

Maybe it is a 30-hour week.

Maybe different professions develop completely different arrangements.

Maybe we move away from measuring professional work primarily through time and toward something much closer to outcomes.

There probably won’t be one answer.

But there is one question I think every employer—and every employee—should begin asking:

When AI gives us time back, who owns it?

Because the answer cannot automatically be:

The employer.

If AI allows Tuesday’s work to be finished Monday, and Friday’s work to be finished Wednesday, we have created something enormously valuable.

Not merely productivity.

Time.

Time may ultimately prove to be the most important dividend of the AI revolution.

The question is whether companies will treat that dividend as another resource to extract—or something their people deserve to share.

Perhaps the 40-hour workweek survives the age of agentic AI.

But for the first time in generations, employers may have to make a much stronger case for why it should.

Our best days lie ahead.

Note: image in this post rendered on ChatGPT based on our prompts.

I’m Dan Smolen. As host and executive producer of What’s Your Work Fit? I help you make your work and workplace decisions result in better and more satisfying professional experiences and outcomes. I am also a Founding Member of The Future of Work Alliance.

Join What's Your Work Fit? on LinkedIn

Join What's Your Work Fit? on YouTube

Likely you’ve heard it: the popular prediction about AI and the workplace that goes something like this:

AI will flatten organizations.

Managers—especially middle managers—will disappear.

With intelligent agents tracking projects, summarizing meetings, analyzing performance, coordinating schedules, preparing reports and moving information throughout an organization, companies simply won’t need as many people managing other people.

There is undoubtedly some truth in that.

But I wonder if we’re asking the wrong question.

Instead of asking how many managers AI will eliminate, perhaps we should ask:

What if AI eliminates much of what made management such a miserable job in the first place?

A remarkable amount of what we call management isn’t really managing people.

It’s managing information.

And AI is extraordinarily good at that.

The middle manager became the human application programming interface (API)

Think about the typical day of a middle manager.

There are calendars to coordinate.

Meetings to schedule.

Meetings to attend.

Meeting notes to summarize.

Status reports to prepare.

Dashboards to update.

Projects to track.

Performance metrics to compile.

Emails to answer.

Information from senior leadership to translate downward.

Information from teams to summarize upward.

And then there are the meetings about the meetings.

Somewhere in the middle of all of this is supposedly the actual work of managing people.

That includes helping a talented employee who has lost confidence.

Coaching someone through an assignment they have never handled before.

Resolving a conflict between two valuable team members.

Recognizing that a high performer is burning out before the high performer recognizes it.

Helping a new employee understand how an organization really works.

Giving someone difficult feedback without destroying their confidence.

Recognizing potential.

Encouraging experimentation.

Creating psychological safety.

Helping people find meaning in their work.

These are fundamentally human activities.

Yet in many organizations, they have been squeezed into whatever time remains after the administrative machinery of management has been fed.

We shouldn’t be surprised that so many workers complain that their managers don’t manage them very well.

Their managers may barely have time to do it.

AI could remove the administrative debris

Agentic AI changes this equation dramatically.

Imagine the manager whose AI agents can continuously track project status without requiring another status meeting.

They can summarize the important points from team discussions.

They can identify approaching deadlines.

They can surface unusual patterns in workflow.

They can prepare the first draft of a performance summary.

They can coordinate schedules.

They can find information buried inside company systems.

They can remind people about commitments.

They can prepare briefing materials before a meeting rather than requiring the manager to spend an hour assembling them.

None of this sounds revolutionary individually.

Collectively, however, it could be.

We may be talking about removing hours of administrative debris from a manager’s week.

And that raises a much more interesting question:

What should we do with the time AI gives back?

There are two very different answers.

Companies can eliminate managers—or reinvent them

Some organizations will see AI efficiencies and immediately reach for the org chart.

If one manager can oversee twice as many people, why not double the span of control?

If AI can handle much of the coordination work, why not eliminate an entire management layer?

And certainly, some management jobs probably will disappear.

Organizations accumulate bureaucracy. Titles proliferate. Layers get created that eventually become difficult to justify. AI may expose some of that.

But there is another possibility.

The manager stops being primarily an information router.

The manager becomes a coach.

A mentor.

A translator of ambiguity.

A developer of talent.

A resolver of conflict.

A builder of trust.

A person who notices things that don’t appear on dashboards.

And perhaps most importantly, someone who helps another human being become better at what they do.

That sounds less like traditional middle management. It sounds much more like leadership.

This becomes especially important in the AI workplace

There is a fascinating contradiction emerging around AI.

As machines become more capable, the human parts of work may become more valuable rather than less.

Consider the young professional entering an AI-supported workplace.

AI can provide enormous amounts of information.

It can explain concepts.

It can draft documents.

It can analyze data.

It can recommend courses of action.

But it cannot assume organizational responsibility for helping that young person develop judgment.

The human advantage:

Someone still needs to say: “Technically, that’s correct. But here’s why I wouldn’t do it.”

Someone needs to explain why an angry client’s email doesn’t necessarily require an angry response.

Someone needs to recognize when a young employee is following the AI recommendation too literally.

Someone needs to explain organizational context.

Someone needs to ask the question the machine didn’t ask.

Someone needs to help that employee distinguish between getting an answer and exercising judgment.

That is management.

Or at least, it should be.

The best managers may become talent multipliers

This could fundamentally change how we measure management performance.

For decades, we have often rewarded managers for outputs that are easy to measure:

Did the project ship?

Did the team hit its numbers?

Did costs remain within budget?

Did everyone complete the required processes?

Those things still matter.

But AI may make many of those administrative outcomes easier to achieve.

The differentiator may increasingly become something harder to quantify:

What happened to the humans you managed?

Did they become more capable?

Did their judgment improve?

Did they learn new skills?

Did strong performers stay?

Did struggling performers get the coaching they needed?

Did people become more confident making difficult decisions?

Did the team become capable of operating with greater autonomy?

Did people leave your team better prepared for their next opportunity than when they arrived?

Those aren’t soft outcomes.

In an economy in which human judgment, creativity, discernment and adaptability become increasingly valuable, they may be among the most important outcomes a manager produces.

But leadership has to resist the productivity trap

There is an obvious danger here.

AI gives a manager ten hours back each week.

Leadership sees ten available hours.

So leadership fills them.

More direct reports.

More projects.

More meetings.

More responsibility.

More output.

The organization captures every minute of the AI dividend and converts it into additional productive capacity.

We’ve seen this pattern before.

Technology promises to reduce work, and organizations discover new ways to fill the space technology creates.

But if we do that with management, we may squander one of AI’s greatest opportunities.

The purpose of reclaimed hours shouldn’t be more management. It should be better management.

That requires leadership teams to make a conscious decision about what they believe managers are actually for.

What if managers were measured by the people they developed?

Imagine a company making this explicit.

A manager’s job description might say:

Your responsibility is not merely to supervise the output of your team.

Your responsibility is to increase the productive capacity of your team.

AI handles much of the tracking.

AI handles much of the summarizing.

AI handles much of the scheduling.

AI handles much of the reporting.

The manager handles the messy human stuff.

The conversation that can’t be reduced to a prompt.

The disagreement that doesn’t have a clean answer.

The employee whose performance problem is actually a confidence problem.

The talented person who needs someone to say, You’re ready.

The person going through something difficult who needs a little grace.

The employee with an unconventional idea who needs someone willing to listen.

The emerging leader who needs someone to challenge them.

The team that needs someone to remind them why their work matters.

That’s not administrative overhead. That’s leadership infrastructure.

Maybe we’re witnessing the end of one kind of middle management

I don’t doubt that AI will eliminate some management positions.

It probably should.

There are organizational layers whose primary purpose is moving information from one level of a company to another. AI will increasingly make that unnecessary.

But eliminating bureaucratic management isn’t the same thing as eliminating managers.

We may instead be separating two jobs that industrial organizations gradually mashed together: managing information and managing humans.

Machines are becoming extraordinarily capable at the first.

Humans may finally have more time for the second.

So perhaps the question for CEOs, boards and workplace leaders isn’t:

How many managers can AI help us eliminate?

Maybe it’s:

Now that AI can do so much of the work we never really needed managers to do, what do we finally want our managers to become?

That’s a much more interesting conversation.

And it may tell us a great deal about whether the AI workplace ultimately produces better work—or simply more of it.

Our best days lie ahead.

Note: image in this post rendered on ChatGPT based on our prompts.

I’m Dan Smolen. As host and executive producer of What’s Your Work Fit? I help you make your work and workplace decisions result in better and more satisfying professional experiences and outcomes. I am also a Founding Member of The Future of Work Alliance.

Join What's Your Work Fit? on LinkedIn

Join What's Your Work Fit? on YouTube

Since workers left farm and field for industry, we have organized much of working life around time.

You show up. You work your hours. You go home.

The 40-hour workweek became so embedded in American working culture that we rarely stop to consider the bargain underneath it.

Because if AI agents can perform in minutes work that once consumed hours—researching, analyzing, scheduling, producing reports, preparing presentations, writing code, responding to customers, managing workflows—then something very important happens:

The relationship between time and output begins to break down.

And that leaves us with a question much bigger than whether the 40-hour workweek survives.

What will employers do with the human time that AI gives back?

There are two very different futures ahead of us

Imagine a professional whose job today requires roughly 40 hours of work each week.

Now imagine that, through the effective use of AI agents, that person and their organization can produce the same amount of useful output in 30 hours.

What happens to the other ten?

One possibility is extraordinary.

The company says: We are accomplishing what we need to accomplish. Go live your life.

Maybe the workweek becomes 32 hours.

Maybe Friday disappears.

Maybe people work intensely for portions of the week and have much greater flexibility during the rest.

Maybe parents get more time with their children. Caregivers get more breathing room. People exercise, volunteer, learn, create, rest or simply spend more time doing things that have nothing whatsoever to do with increasing shareholder value.

That could be one of the great dividends of artificial intelligence.

It could also help millions of people achieve something closer to their Work Fit: work that fits who they are, how they want to live and the contribution they want to make.

But there is another future. And I suspect plenty of people reading this already know what it looks like.

The employer says:

Great. You finished that work in 30 hours.

Here’s ten more hours of work.

Productivity gains have an owner

This gets us to a question I raised earlier this week and can’t stop thinking about.

Who owns the AI productivity dividend?

If technology makes a worker twice as productive, who gets the benefit?

The customer?

The shareholder?

The employer?

The employee?

Or some combination of all four?

That question may become one of the defining workplace debates of the AI era.

AI doesn’t simply create greater productivity. It helps to establish our productive capacity.

Those are not quite the same thing.

Productivity tells us that we can accomplish more with fewer inputs.

Productive capacity asks what we choose to do with the capability we have created.

And that is a leadership decision.

Public companies may face the hardest choice

This is where the discussion gets uncomfortable.

Imagine two publicly traded competitors.

Both adopt agentic AI.

Both discover that their employees can accomplish substantially more in substantially less time.

Company A uses part of that dividend to improve people’s working lives. It shortens the workweek, invests in development, creates breathing room for experimentation and allows employees greater agency over their time.

But Company B sees something else: More revenue.

If our people can produce 25 percent more, why settle for the same output?

Why not increase sales targets?

Why not increase client loads?

Why not reduce headcount and distribute the remaining work among fewer people?

Why not demand faster product cycles?

Why not turn every hour AI saves into another hour of human productive capacity?

If Company B produces stronger quarterly results because of those choices, what happens to Company A? That is the issue.

The market may reward the organization that extracts the greatest immediate economic value from AI—not necessarily the organization that distributes some of that value back to the people creating it.

A CEO who tells investors, “AI allowed us to give thousands of employees Friday afternoons back” may have created enormous human value.

But another CEO may tell investors:

“AI allowed us to increase output 30 percent while reducing our workforce 15 percent.”

Which announcement gets rewarded? We shouldn’t pretend we know the answer in every case.

But we should be asking the question.

The 40-hour week could survive for exactly the wrong reason

This is why I am not yet convinced that AI automatically leads us toward the four-day workweek.

Technologically, the argument may become easier.

If organizations can produce the same output in dramatically less human time, maintaining a five-day, 40-hour schedule becomes increasingly difficult to justify on productivity grounds alone.

But economically?

The opposite pressure could emerge.

The 40-hour week could remain firmly in place while the amount of output expected during those 40 hours increases dramatically.

That would give us a strange outcome.

We would have created technologies capable of liberating people from enormous amounts of routine work—and used them instead to make people work harder.

The workweek wouldn’t shrink. It would become denser.

Forty hours of work in the AI age could feel very different than we are use to working.

More decisions.

More projects.

More clients.

More responsiveness.

More measurement.

More expectations…and

Less recovery time between tasks because the technology eliminated the natural pauses that once existed within work.

That doesn’t sound like liberation. It sounds like old school industrial-style management with much better software.

There is also an economic argument for giving people time back

The case for sharing the AI dividend with workers doesn’t have to rest entirely on benevolence.

People are not machines.

There is a limit to the number of consequential decisions we can make well.

There is a limit to how much complexity we can absorb.

There is a limit to how many clients, projects, conversations and problems we can manage simultaneously before the quality of our judgment begins to decline.

And that matters even more in an AI-supported workplace.

As machines assume more routine execution, human value may increasingly concentrate around judgment, discernment, creativity, empathy, leadership, relationship-building and asking good questions.

Those capabilities do not necessarily improve when every available minute is filled.

Quite the opposite.

Perhaps we need a new bargain

I don’t think the answer is simply legislating a 32-hour workweek.

Nor do I think every company or every kind of work can operate on the same schedule.

Healthcare isn’t advertising.

Manufacturing isn’t consulting.

Hospitality isn’t software development.

And a boot-strapped startup fighting for survival operates differently from a mature enterprise.

But we should stop treating the workweek as an immutable law of nature.

It isn’t.

It is an arrangement.

And arrangements can change when the conditions underlying them change.

Agentic AI is changing those conditions. That means employers, workers, boards and investors may soon have to negotiate a new bargain.

Perhaps employees receive some of the productivity dividend as higher compensation.

Perhaps customers receive some through lower prices or better service.

Perhaps shareholders receive some through increased profitability.

And perhaps workers receive something that has rarely appeared on a corporate balance sheet: time.

This is ultimately a leadership question

Every company adopting agentic AI is going to make this decision, whether its leaders recognize it or not.

What happens when AI saves an employee an hour?

Do we immediately refill it?

What happens when it saves a day?

What happens when an entire team can accomplish its previous workload by Thursday afternoon?

Do we celebrate the achievement and send them home?

Or, do we address the backlog?

Those seemingly small choices will eventually reveal something fundamental about an organization.

Does it see AI as a way to improve work—or primarily as a way to extract more work from humans?

I believe that distinction will matter enormously.

Once people understand that bargain, why would they embrace the next productivity tool?

So, what should happen to the workweek?

I don’t know.

And that’s exactly why I think we need to start having this discussion now.

Agentic AI is giving organizations an extraordinary new productive capacity.

We can use that capacity to produce more.

We can use some of it to work less.

Or we can attempt to do both.

But we shouldn’t allow the answer to emerge accidentally from quarterly earnings pressure, utilization targets and the assumption that every efficiency must immediately become additional output.

Because underneath the debate about the four-day week is a much more consequential question about the future of work:

If technology allows us to accomplish dramatically more with dramatically less human effort, should humans actually benefit from that progress?

Or will the reward for becoming more productive simply be more work?

I’d especially like to hear from leaders, employees, investors and board members:

If agentic AI gives a company 20 percent more productive capacity, how much of that dividend—if any—should belong to the people who work there?

That may be one of the defining Work Fit questions of the next decade.

Our best days lie ahead.

Note: image in this post rendered on ChatGPT based on our prompts.

I’m Dan Smolen. As host and executive producer of What’s Your Work Fit? I help you make your work and workplace decisions result in better and more satisfying professional experiences and outcomes. I am also a Founding Member of The Future of Work Alliance.

Join What's Your Work Fit? on LinkedIn

Join What's Your Work Fit? on YouTube

There has been a fairly understandable bargain between employer and employee.

The employee gives their time, skills, judgment and effort.

The employer provides the employee compensation, benefits and some measure of security.

The company takes what all in the workplace produce, sells it to customers, pays its expenses, invests in its future and—hopefully—creates a return for its shareholders.

It has never been quite that simple, of course. And it has certainly never been perfectly equitable. But we understood the bargain.

I wonder if Agentic AI is about to break it.

Because something fundamentally different happens when technology doesn’t merely make a factory, warehouse or computer system more productive.

It makes me more productive.

And perhaps dramatically so.

Imagine that I can suddenly do twice as much

Consider a career professional who spends a considerable part of their work week gathering insight, preparing reports, summarizing meetings, analyzing data, drafting presentations, responding to routine correspondence, and moving work product through various processes.

Now give that person increasingly capable AI agents.

Some of those tasks that consumed hours may require mere minutes.

Some may disappear from the employee’s workload altogether.

Let’s not get hung up on whether the AI-fueled productivity improvement is 20 percent, 50 percent or 100 percent. It will vary enormously by job, industry and individual.

The important thing is that something valuable has been created: Productive Capacity.

And that capacity has economic value.

Which raises a question I don’t think we’re discussing nearly enough:

Who owns my Productive Capacity?

If Agentic AI allows an employee to produce in 25 hours what previously required 40, does their employer automatically get another 15 hours of output?

Does the employee get some of that time back?

Do customers benefit through lower prices or better service?

Do shareholders receive the benefit through higher margins?

Or should the dividend somehow be shared among all of them?

This may become one of the defining workplace questions of the AI era.

We know how this usually plays out

History provides workplace talent plenty of reason to be skeptical.

For generations, technological improvements have allowed companies to make things faster, cheaper and with fewer people.

The benefits have been enormous. Consumers have gained access to products and services previous generations couldn’t have imagined. Companies have created extraordinary wealth. Entire industries have emerged.

The tech that was supposed to eliminate drudgery simply increases the amount of work that can be packed into the day.

And eventually we arrive at an absurd outcome:

Agentic AI becomes exponentially more capable, yet humans feel busier than ever.

That’s not a technology problem, it’s a leadership decision.

I’ve written recently about what companies should do with the time Agentic AI creates.

And, I’ve argued that leaders should think about productive capacity rather than simply squeezing more human efficiency out of people.

But I think the issue goes deeper than that.

We’re renegotiating the basic bargain of employment.

Companies desperately want people to embrace Agentic AI. But their management practices could simultaneously teach their head count to hide the benefits of using it.

That’s a terrible way to build a trust based AI-enabled workplace.

There needs to be an AI productivity dividend

Perhaps we need a different idea.

Call it the AI productivity dividend.

When AI increases productive capacity, everyone participating in the economic system should benefit.

Shareholders should benefit. There’s nothing inherently wrong with that. Companies invest enormous sums in technology precisely because they expect a return.

Customers should benefit through better products, better service, greater innovation or perhaps lower costs.

The company should benefit through stronger competitiveness and profitability.

But employees should benefit, too.

And the employee’s dividend doesn’t necessarily have to arrive entirely in their paycheck.

It could mean more compensation.

But it could also mean more control over time.

A shorter workweek.

More flexibility.

More opportunity to learn.

Time to experiment with ideas that don’t have an immediate quarterly return.

Greater investment in professional development.

More human interaction with customers and colleagues.

More room for mentoring.

Or simply fewer hours spent doing things that nobody particularly enjoys doing.

That last one shouldn’t be dismissed. Removing some misery from work is an economic benefit, too.

What if the dividend was time?

This is where the discussion gets particularly interesting to me.

We’ve spent more than a century treating time as one of the primary currencies of employment. But Agentic AI challenges the relationship between time and output.

If two people can achieve the same high-quality outcome and one requires 40 hours while another, effectively using AI, requires 25, what exactly are we purchasing when we employ them?

Their time?

Or their contribution?

That distinction matters.

If we’re purchasing contribution, then employees should logically have a claim on some of the time that technology gives back. And that could have profound implications for Work Fit.

Work Fit isn’t about working less

I want to be careful here.

The argument for Work Fit isn’t that everyone should work as little as possible.

Many people love working.

They want to build things.

Solve difficult problems.

Lead teams.

Start companies.

Serve customers.

Master their craft.

Earn more money.

Clearly, make a positive difference.

Work can provide positive meaning, identity, community and tremendous satisfaction.

The question is whether work fits the life someone is trying to live.

And Agentic AI potentially gives us an extraordinary opportunity to improve that fit.

Imagine parents having more opportunities to be present in their kids’ lives.

Imagine an experienced professional having time to mentor younger colleagues rather than spending hours preparing presentation decks.

Imagine younger workers having time to develop judgment and expertise rather than racing from task to task.

Imagine someone in their 50s being able to remain highly productive while gradually reducing their hours instead of choosing between full-time work and retirement.

Imagine people using some of their AI-created capacity to think.

Truly, to think.

Not respond.

Not process.

Not produce another deck.

Think.

What might that be worth to an organization?

The shareholder isn’t the only stakeholder

Corporate leadership and boards have an important role here.

They will understandably ask what AI investment does for their margins, revenue growth, productivity and shareholder value.

They should.

But I hope someone in those boardrooms asks another question:

What portion of our productivity dividend goes to the very people whose work created it?

That isn’t anti-business.

I think it’s very much a business question.

Companies that share their AI dividend may have an enormous advantage in attracting and retaining talented people.

Imagine two competing employers.

Company A says:

“We’ve invested heavily in AI, and because you’re now more productive, we’re raising your targets by 35 percent.”

Company B says:

“We’ve invested heavily in AI. We expect stronger performance, but we also want some of the capacity we’ve created to come back to you—in flexibility, development, compensation and time.”

Which company attracts the better talent pool?

Which company earns more discretionary effort?

Which one creates greater trust?

Which one is more likely to get employees enthusiastically searching for the next thing they can automate?

I think you know.

We have a choice

There is nothing inevitable about how Agentic AI changes modern work and the workplace where it happens.

That’s important.

Technology doesn’t decide whether someone gets more autonomy.

Technology doesn’t decide whether the work week gets shorter.

Technology doesn’t decide whether productivity gains go entirely to shareholders.

Technology doesn’t decide whether employees become more engaged or more exhausted.

People make those decisions.

Executives make them.

Boards make them.

Managers make them.

And, increasingly, employees will make them when deciding where—and how—they want to work.

Agentic AI is giving organizations something extraordinarily valuable: productive capacity.

We can use all of it to produce more.

Or we can recognize that some of the dividend might be better invested in producing better work and better working lives.

That’s the grand bargain that I think we need to start discussing.

Because perhaps the most important question about AI and work isn’t:

How much more can people produce?

It’s this:

When technology gives us some of our human capacity back, who gets to decide what we do with it?

Folks, that’s a Work Fit question.

And increasingly, it’s going to be everyone’s question.

Our best days lie ahead.

Note: image in this post rendered on ChatGPT based on our prompts.

I’m Dan Smolen. As host and executive producer of What’s Your Work Fit? I help you make your work and workplace decisions result in better and more satisfying professional experiences and outcomes. I am also a Founding Member of The Future of Work Alliance.

Join What's Your Work Fit? on LinkedIn

Join What's Your Work Fit? on YouTube

Something interesting is happening in communities across America.

Plenty of people in the middle simply look at a massive windowless building rising near their neighborhood and ask:

Who decided building a data center here was a good idea?

The backlash is becoming remarkably bipartisan.

And perhaps it should be.

Because the debate over data centers isn’t simply a debate about artificial intelligence.

It is becoming a debate about place.

I Have Seen What Happens When the Data Centers Arrive

I experienced this firsthand in Northern Virginia.

For years, Northern Virginia has been the epicenter of the global data center industry. And in Prince William County, where I live, the transformation has happened with astonishing speed.

Places that once felt bucolic became something altogether different.

Whole forests were felled. Huge buildings appeared. Electrical infrastructure expanded. Landmarks disappeared.

What emerged in some parts of my community feels like a Brutalist architecture hellscape: enormous, largely featureless structures occupying acreage at a scale that is difficult to comprehend until you live near them.

But aesthetics are only the beginning of the conversation.

My neighbors and I have raised questions about electricity reliability and costs. Others worry about water. Still others worry about diesel generators, air pollution, high decibel noise, overhead transmission lines, and what happens when enormous new electrical loads are added to an already complicated grid.

Some of those fears may prove justified.

Others may not.

And that distinction matters.

Virginia’s Joint Legislative Audit and Review Commission, for instance, concluded in its major study of the industry that data center water use in Virginia was currently sustainable, although growing and in need of better management.

It also concluded that growing electricity demand from data centers could require enormous investments in generation and transmission, potentially increasing costs for other electricity customers.

Virginia environmental regulators, meanwhile, are now conducting special air monitoring in Loudoun and Prince William counties in response to community concerns about emissions from backup generators and natural-gas turbines.

Those are precisely the kinds of facts communities need.

Here’s the Complication: I Believe in AI

This is where my argument may disappoint people looking for an easy villain.

I am profoundly interested in the potential of agentic AI to improve work.

I’ve spent years studying, writing and talking about the future of work and the modern workplace. And I believe AI could help millions of people shed some of the monotonous, repetitive and error-prone tasks that consume their working lives.

Think about what becomes possible when machines handle more of that work.

People may gain more time to exercise judgment.

More opportunity to solve problems.

More capacity to think critically.

More reason to ask interesting questions.

More agency over how they create value.

At its best, agentic AI could help move us toward something I’ve long described as Work Fit: work that better fits who we are, how we want to live and what we hope our lasting contributions will be.

That possibility excites me.

But AI doesn’t exist in the cloud.

The cloud is buildings.

It is substations.

It is transmission infrastructure.

It is cooling systems.

It is generators.

It is water.

It is electricity.

And increasingly, it is enormous parcels of land located alongside somebody’s residential subdivision.

That physical reality needs to become part of our AI conversation.

Every Transformative Technology Creates a Bill to Pay

We’ve been through versions of this before.

The personal computer dramatically increased human capability.

So did the smartphone.

So did social media.

Each produced extraordinary benefits.

And each produced consequences that were either underestimated or barely contemplated when adoption began accelerating.

We discovered the consequences later.

Sometimes decades later.

AI may follow the same pattern as personal computers and smartphones, except AI’s infrastructure requirements are occurring at extraordinary scale and speed.

The U.S. Energy Information Administration reported this year that commercial electricity sales in Virginia have soared, driven by data centers. PJM expects the Dominion zone serving Virginia to experience the largest absolute increase in summer peak electricity demand between 2026 and 2030, largely because of data center growth.

This isn’t some hypothetical conversation about 2045.

It is happening now.

Which means communities getting their first data center proposals today have an advantage Northern Virginia didn’t have.

They can learn from us.

Don’t Ask Only: “Do We Want a Data Center?”

That question is far too simplistic.

Local elected officials are frequently presented with an enticing economic-development proposition.

Investment.

Construction.

Tax revenue.

Technology.

Economic competitiveness.

Those benefits deserve consideration.

But residents should insist that another ledger sit alongside the economic-development ledger. Call it the community impact ledger.

What will happen to electricity demand?

Who pays for new generation and transmission?

How much water will the development require—not merely on an average day, but during periods of peak demand?

What backup generation will be installed?

What emissions could result?

What will residents hear from their homes?

What will they see?

How close will industrial-scale facilities be permitted to homes, schools, parks, historic sites and environmentally sensitive areas?

How many permanent jobs will remain after construction?

What tax incentives are being offered?

And perhaps most importantly:

What happens if the assumptions presented to the community turn out to be wrong?

The Questions Every Community Should Ask

If a data center development is being proposed where you live, I think citizens should ask their elected officials questions like these:

Show us the energy math. How much electricity will the completed development require at peak operation? What new generation, transmission or substation infrastructure will be necessary? Who will pay for it?

Show us the water math. How much potable water could the facility consume annually and during the hottest periods of the year? Where will that water come from? What happens during drought conditions?

Show us the environmental math. What emissions, noise, stormwater and ecosystem effects have been modeled—and who conducted the modeling?

Show us the taxpayer math. What tax incentives or exemptions are being provided? What public infrastructure will be required? What is the projected net fiscal benefit after those costs are included?

Show us the jobs math. Separate construction employment from permanent employment. How many people will actually work at the facility once it is operational?

Show us the alternatives. Why this parcel? Why this size? Could greater setbacks, landscaping, architectural requirements or a different location substantially reduce community impact?

Show us what you know before you vote. What studies have elected officials, planning commissioners, utilities and developers seen that the public hasn’t?

And one more:

Give citizens enough time to understand the answers.

A public hearing held shortly before a consequential land-use vote is technically public participation.

That doesn’t necessarily make it meaningful public participation.

Oh, and one more thing: let’s make sure mainstream media cover these hearings.

Transparency Should Be Infrastructure, Too

We spend enormous amounts of time debating the infrastructure AI requires.

Power.

Fiber.

Chips.

Cooling.

Transmission.

But there is another infrastructure necessary for technologies operating at this scale:

Trust.

Trust requires transparency.

Imagine if every major proposed data center development came with a standardized public dashboard showing projected electricity demand, water requirements, backup generation, emissions, tax incentives, permanent employment, transmission requirements and environmental impacts.

Not a developer’s marketing presentation.

Not an opposition group’s worst-case scenario.

Comparable public data.

Let citizens see the tradeoffs.

Because there will be tradeoffs.

This Doesn’t Have to Become an Anti-AI Movement

I hope it doesn’t.

AI could become one of the most consequential tools humans have ever created.

It could make medicine better.

Science faster.

Government more responsive.

Businesses more productive.

And, most interesting to me, work more human.

Technological progress shouldn’t require communities to surrender their ability to determine what kind of places they want to inhabit.

We should be capable of holding two ideas simultaneously:

We need the infrastructure that makes AI possible.

And:

Communities have a right to determine how much of that infrastructure they can reasonably absorb.

Those aren’t contradictory positions.

They are what responsible technological adoption looks like.

Watch my video about living 100 yards from a data center under construction.

Northern Virginia Should Be a Case Study for the Nation

For communities encountering their first major data center proposals, Northern Virginia offers something valuable.

Gain a glimpse of the future.

Come here.

Drive around Ashburn, Manassas, and Gainesville.

Look at the buildings.

Look at the transmission infrastructure.

Talk to residents.

Talk to people who support the industry.

Talk to people who oppose it.

Talk to local officials.

Then go home and decide what you want your community to look and feel like 10 or 20 years from now.

Because once development reaches a certain scale, some decisions become extraordinarily difficult to reverse.

AI may fundamentally change how we work.

I believe some of those changes could be wonderful.

But the future of work cannot be separated from the future of place.

After all, when the workday ends, we still have to live somewhere.

And before we transform those places to power our extraordinary technological future, communities deserve the opportunity to ask one very human question:

What do we want this place to become?

When we know then our best days lie ahead.

Image credit: Dan Smolen, April 2026.

I’m Dan Smolen. As host and executive producer of What’s Your Work Fit? I help you make your work and workplace decisions result in better and more satisfying professional experiences and outcomes. I am also a Founding Member of The Future of Work Alliance.

Join What's Your Work Fit? on LinkedIn

Join What's Your Work Fit? on YouTube

I have been thinking a lot lately about my friend and colleague Helayna Minsk and her thought-leadership around corporate boards.

Helayna challenges an old and stubborn conception of the board: a collection of accomplished people who gather periodically, review what management puts in front of them, approve the important things, question a few others, and ultimately provide their blessing to the CEO.

The proverbial rubber stamp.

Her vision is much more interesting.

The modern board should be intentional and forward-looking. It should help guide the purpose of the enterprise, challenge management’s assumptions, anticipate what is coming around the corner, and help ensure that the company creates positive outcomes while also growing revenues and shareholder value.

And lately, her thinking has me wondering about something else.

What responsibility should corporate boards have for the introduction of agentic AI into the workplace?

Not simply its cybersecurity.

Not simply its legal risks.

Not simply how much money it can save.

And certainly not simply how many people it might replace.

Boards should be asking a much larger question:

What kind of company do we want AI to help us become?

Because that decision is being made right now.

AI deployment is becoming organizational design

For the first few years of generative AI, companies could reasonably treat AI as a technology issue.

Which tools should employees use?

What information could they put into them?

How could AI improve productivity?

Agentic AI changes the stakes.

AI agents increasingly won’t merely help people complete individual tasks.

They will perform sequences of work, interact with systems, gather information, make recommendations, initiate actions, and increasingly operate alongside human talent.

That means AI implementation is becoming something much bigger than technology deployment.

It is becoming organizational design.

Every time an organization gives work to an AI agent, it implicitly decides what work remains for a human being.

Every time AI accelerates a process, someone decides what happens to the time that acceleration creates.

Every time AI provides an employee with vastly greater access to knowledge, someone decides whether that employee gains greater authority to use it.

And every time AI eliminates repetitive work, someone decides whether the worker receives more interesting work—or simply more work.

Those aren’t IT decisions. Ultimately, they are governance decisions.

Boards need to ask where the AI dividend goes

This may become one of the defining corporate governance questions of the next decade.

Agentic AI should create an enormous time dividend.

Processes that once took days may take hours. Tasks that took hours may take minutes. Research, analysis, administrative work and coordination may become dramatically easier.

Where does that dividend go?

The conventional answer will be obvious:

Productivity.

If ten people can accomplish what once required twelve, reduce headcount.

If a worker can complete a task 30 percent faster, give that worker 30 percent more work.

If AI increases capacity, immediately convert that capacity into output.

Some of that will happen. And some of it should.

Companies exist to produce economic value.

That isn’t necessarily good governance.

And over time, it may not even be good economics.

Ask whether AI is increasing human capacity

There is another way to measure the return.

Boards could ask management to demonstrate how agentic AI is increasing what I would call the productive capacity of the people in the organization.

Not merely their productivity.

Their capacity.

There is an important distinction.

Did AI give a junior employee access to information that once belonged mostly to senior executives?

Did it help a manager recognize a problem sooner?

Did it give someone enough time to learn a new skill?

Did it enable employees to test ideas they previously lacked the resources to explore?

Did it allow teams to spend more time with customers?

Did it make people better at exercising judgment?

Did it remove enough administrative drudgery that people could spend more of their working lives doing things that actually require human beings?

Those outcomes may be harder to capture on a quarterly earnings statement. But they may be enormously important to the long-term health of the enterprise.

Boards should demand an answer to five questions

As agentic AI moves deeper into organizations, I believe directors should repeatedly challenge leadership around five things.

What work are we removing from people?

Automation should have a purpose. Boards should understand whether AI is primarily removing drudgery, eliminating jobs, augmenting expertise or fundamentally restructuring roles.

What are people gaining in return?

If employees save five hours a week through AI, what happens to those five hours? Do they gain learning, experimentation, customer interaction, collaboration or some greater measure of control over their work?

Or does the workload simply refill the space?

Is AI expanding agency or reducing it?

This may be the most important question.

AI can create workplaces in which people have extraordinary information at their fingertips and greater ability to make decisions.

It can also create workplaces in which algorithms monitor, prescribe and constrain nearly everything people do.

Those are two radically different futures of work.

Boards should know which one their company is building.

Are we making people more capable—or more dependent?

An employee who accepts every AI recommendation without understanding it isn’t necessarily becoming more valuable.

People still need judgment, curiosity, critical thinking and discernment.

The best AI environments should strengthen those human capabilities rather than quietly atrophy them.

Are people actually experiencing better work?

Employee engagement surveys won’t be enough.

Boards should want to understand whether AI is affecting workload, burnout, learning, autonomy, mobility and people’s sense that their work matters.

If management reports enormous AI productivity gains while employee exhaustion is simultaneously increasing, directors should become very curious.

AI governance needs a human dashboard

Boards will naturally receive AI dashboards.

They will see capital expenditures, adoption rates, productivity improvements, security incidents, cost savings and return on investment.

Good.

They should.

Perhaps next to that AI dashboard should be another one. Call it the Human Capacity Dashboard.

It might track internal mobility.

Skills gained.

Time spent learning.

Employee autonomy.

Manager spans of control.

Workload.

Burnout.

Retention of high-performing talent.

Employee confidence using AI.

Perhaps even something as simple as the percentage of workers who believe AI has made their jobs better.

Imagine the conversation that metric could generate in a boardroom.

Management announces that its AI program generated $200 million in productivity improvements.

A director responds:

“That’s impressive. What percentage of our employees say AI improved the quality of their working lives?”

That is a very different governance conversation.

Happiness isn’t a soft issue

I can already hear the objection.

Corporate boards aren’t responsible for making employees happy.

Fair enough.

A board isn’t an employee wellness committee.

But happiness at work shouldn’t be dismissed as ping-pong tables, free lunches and feel-good HR programs.

Think instead about whether people experience agency, mastery, meaning and sustainable workloads.

Those things affect retention.

They affect innovation.

They affect institutional knowledge.

They affect customer relationships.

They affect whether talented people want to join an organization and whether the best people want to stay.

Human flourishing and shareholder value don’t have to occupy opposite sides of the table.

The interesting governance question is how often they can reinforce one another.

The board can protect the long term from the quarter

And perhaps this is where boards become particularly important.

Executives operate under tremendous short-term pressure.

Revenue targets must be hit.

Margins must improve.

Analysts must be satisfied.

Shareholders expect returns.

AI will present management with irresistible opportunities to generate near-term efficiencies.

Someone needs to keep asking about the enterprise five years from now.

What happens if we remove too many entry-level positions?

Where will our future senior talent come from?

What happens if people stop learning because AI performs all of the developmental work?

What happens if managers oversee algorithms rather than mentor people?

What happens if we gain efficiency while losing institutional knowledge?

What happens if our highest performers conclude that AI has simply enabled us to demand more from them?

And perhaps most importantly: What happens if we create an extraordinarily efficient company that nobody particularly wants to work for?

Those are precisely the kinds of questions a forward-looking board should ask.

This is where purpose becomes real

Corporate purpose is relatively easy to discuss when conditions are stable.

It becomes meaningful when leaders must make difficult choices.

Agentic AI will create many of them.

Companies will have extraordinary opportunities to eliminate costs, increase output and restructure their workforces.

Some will use those capabilities almost exclusively to extract more productivity from fewer people. Others may make a different bargain.

They may decide that some portion of the AI dividend should be reinvested in human beings.

More learning.

More experimentation.

More mobility.

More decision-making authority.

More meaningful work.

Perhaps even more time.

Those organizations won’t reject shareholder value. They may discover a more durable way of creating it.

And that brings me back to Helayna Minsk’s vision of the modern corporate board.

If boards really are evolving from passive overseers into intentional stewards of corporate purpose, then agentic AI may provide one of their greatest tests.

The question before them isn’t simply:

How much value can AI create for this company?

It is also:

What will we do with that value once it is created?

The companies that get agentic AI right may not be the ones with the fewest employees or the highest output per worker.

They may be the companies whose people become more informed, more capable, more discerning and more empowered because AI works alongside them.

And perhaps even a little happier.

Our best days lie ahead.

Note: image in this post was rendered on ChatGPT based on our prompts.

I’m Dan Smolen. As host and executive producer of What’s Your Work Fit? I help you make your work and workplace decisions result in better and more satisfying professional experiences and outcomes. I am also a Founding Member of The Future of Work Alliance.

Join What's Your Work Fit? on LinkedIn

Join What's Your Work Fit? on YouTube

When my daughter was eight or nine years old, she entered her elementary school’s Inventors Fair.

She didn’t spend weeks searching for the perfect idea.

She didn’t pore over design patent databases.

She didn’t hire consultants.

She didn’t attend a startup incubator.

She simply looked around her own home and noticed a problem.

“Mom is always losing her phone.”

This was just as smartphones were becoming part of everyday life. Apple hadn’t yet introduced the Find My feature that many of us now take for granted.

Her solution?

She invented something she called Glowker.

It was a phone cover that glowed bright pink in the dark whenever the phone rang.

She eagerly explained it to me.

“Dad, here’s how it works. Mom can’t find her phone, so you turn out the lights and call her number. The cover lights up, and she finds it.”

Simple.

Practical.

Delightfully clever.

Did she build a working prototype?

No.

Did she raise seed capital from her grandparents?

No.

Did she pitch venture capitalists?

Not even close.

None of that mattered.

What mattered was something much more important.

For perhaps the first time in her young life, she learned that many problems are solvable. Better still, solving them can actually be fun.

That lesson proved far more valuable than any invention itself.

Years later, our family entered an unimaginably difficult chapter. My daughter became seriously ill. Because of complications from Ehlers-Danlos Syndrome and POTS, she spent eighteen months homebound from school.

For many young people, that might have become the defining chapter of their education.

Instead, at her very worst, she kept asking questions.

When she felt up to it, she problem solved.

She kept looking for ways around her physical, emotional, and educational obstacles instead of accepting them as permanent.

Eventually, she returned to high school and graduated with an advanced diploma.

Today, she works as a registered nurse in a busy emergency department where nearly every shift presents a new puzzle to solve on behalf of her patients.

Looking back, I don’t think Glowker was just an elementary school project. It was the beginning of a habit of mind.

As I think about the future of work, I worry that we’ve unintentionally deemphasized precisely this way of thinking.

Schools have become exceptionally good at rewarding correct answers.

Businesses have become remarkably efficient at rewarding flawless execution.

Neither spends enough time cultivating invention.

We tell young people to prepare for careers.

We encourage them to accumulate credentials.

We ask them to memorize information that increasingly sits one prompt away inside an AI model.

But how often do we simply ask:

“What problem have you noticed?”

Or perhaps even more importantly:

“How would you attempt to solve it?”

That may become one of the most valuable questions of the Agentic AI era.

As AI increasingly handles routine production work—writing first drafts, analyzing spreadsheets, generating software code, summarizing research—the economic value of simply producing work begins to fall.

The value shifts upstream.

Toward identifying the right problems.

Toward reframing them.

Toward seeing possibilities others overlook.

Toward imagining solutions no one has yet considered.

I’ve written recently that discernment may become one of the defining workplace capabilities of the Modern Workplace.

I still believe that.

But discernment and invention? They are first cousins.

Discernment helps us recognize which problems actually matter.

Inventiveness helps us imagine what doesn’t yet exist.

Together, they create something extraordinarily powerful.

During my twenty years as an executive recruiter, I met remarkable people across marketing, finance, operations, technology, and communications.

Some of the most memorable candidates I presented to clients weren’t always the smartest people in the room.

Nor were they always the most experienced.

They shared something else.

When describing their work, they almost never started by listing responsibilities or talking about money-motivation.

Instead, they described difficult problems.

They talked about untangling impossible situations.

They explained how they simplified complexity.

They reverse-engineered broken processes.

They connected ideas no one else had thought to connect.

They weren’t motivated by titles.

They were energized by solving hard things.

At the time, I viewed that as an admirable personality trait.

Now I wonder whether it was an early glimpse of what employers will increasingly seek in the AI-infused Modern Workplace.

Because if AI becomes exceptionally good at generating answers, then humans become increasingly valuable for discovering better questions.

Perhaps that’s why elementary school Inventors Fairs are so important.

They’re not really about inventions.

They’re about agency.

They’re about teaching children that the world is not simply something to adapt to.

It is something they can improve.

Imagine if every student graduated believing that every frustration represented an opportunity to invent something better.

Imagine if universities rewarded curiosity as much as specialization.

Imagine if employers hired not only for technical capability but for the habit of seeing possibilities hidden inside everyday problems.

That sounds less like a nice educational philosophy.

It sounds like a competitive advantage.

The future workplace won’t simply reward people who know more.

Increasingly, it will reward those who notice more.

Those who ask better questions.

Those who quietly observe what everyone else accepts as inevitable and then wonder:

“There has to be a better way.”

My daughter called hers Glowker.

The name never mattered.

The mindset always did.

Guided by the spirit of invention, I believe that our best days lie ahead.

Note: image in this post was rendered on ChatGPT based on our prompts.

I’m Dan Smolen. As host and executive producer of What’s Your Work Fit? I help you make your work and workplace decisions result in better and more satisfying professional experiences and outcomes. I am also a Founding Member of The Future of Work Alliance.

Join What's Your Work Fit? on LinkedIn

Join What's Your Work Fit? on YouTube

Our newest workplace entrants have scrupulously followed the counsel of trusted authorities to:

Study hard.

Go to college.

Build marketable skill.

Graduate.

Get that first job.

Establish a career.

It has never been a perfect formula. But for generations, it served as a reliable path into career success.

Now imagine that pathway disappearing.

Not because young people have suddenly become less capable.

Not because they lack ambition.

But because intelligent machines are in the workplace, performing many of the very tasks that once introduced new college graduates to professional work.

That possibility should concern us all.

We are witnessing something different: the gradual disappearance of the first rung on the professional ladder.

How does someone become an experienced professional if no one hires entry-level talent?

This isn’t simply another recession.

During past economic downturns, jobs disappeared because demand weakened.

Eventually businesses recovered.

And hiring resumed.

Agentic AI presents a different challenge.

If millions of capable young adults struggle to enter the workplace, we won’t merely have an employment problem.

We’ll have an agency problem.

So, how do people discover what they’re capable of doing if they never receive the opportunity to contribute?

My daughter followed a different path to work.

Because she was stuck in a [forensic science] degree track that was no longer viable, my Gen Z daughter left her traditional university program and enrolled in nursing school.

Today, she works as a registered nurse in the emergency department of a busy hospital. And she’s crushing it.

Healthcare still depends profoundly on human beings.

Patients need more than information.

They need judgment.

Compassion.

Communication.

Presence.

There will continue to be professions where these distinctly human qualities become even more valuable because AI exists.

But healthcare cannot absorb every graduate. Nor should it.

And that is because America needs:

Architects.

Teachers.

Engineers.

Designers.

Journalists.

Entrepreneurs.

Artists.

Urban planners.

Community builders.

The challenge isn’t deciding whether these professions matter. It’s ensuring that young people can actually begin careers in them.

History reminds us that societies sometimes have to invent new ways of creating meaningful work.

During the Great Depression of the 1930s, unemployment reached 25 percent. So, the United States launched ambitious public programs to put millions of Americans to work.

The Civilian Conservation Corps (CCC) restored forests, built trails, planted trees, and improved parks across the country.

The Works Progress Administration (WPA) employed artists, musicians, writers, engineers, architects, and builders.

These programs relied on experimentation. But, they weren’t perfect.

Some earned criticism.

Others eventually disappeared.

But they reflected an important belief that people deserve the dignity that comes from making meaningful contributions through work.

If AI dramatically reduces traditional entry-level hiring, perhaps we should begin asking what 21st Century versions of those ideas might look like.

Not replicas.

Reimaginations.

Imagine a modern national initiative where young adults spend one or two years helping America tackle problems that the market consistently struggles to solve.

Climate resilience.

Wildfire prevention.

Affordable housing.

Modernizing public infrastructure.

Expanding broadband.

Supporting healthcare systems.

Caring for older adults.

Disaster preparedness.

Cybersecurity for local governments.

Digitizing historical archives.

Improving public transportation.

Community development.

Environmental restoration.

Building more resilient energy systems.

Instead of waiting for careers to begin, young adults would begin by strengthening the country itself.

Perhaps an even broader idea deserves consideration: National Service.

Not necessarily mandatory.

But widely available and highly respected.

Imagine every young American having the opportunity to devote one or two years to serving through healthcare, education, environmental restoration, infrastructure, scientific research, disaster response, technology, or military service.

The return on investment wouldn’t simply be employment.

It would be marketable experience.

Purpose.

Professional maturity.

Civic trust.

Relationships that bridge geography, income, and political identity.

And, of course, the Armed Forces offer young people meaningful career gateways.

For a generation that increasingly reports loneliness and disconnection, those outcomes may prove just as valuable as a paycheck.

But folks, I get it: some reading this Substack may immediately recoil at the idea of New Deal-styled programs providing work, because they believe:

Government shouldn’t create jobs.

Markets should determine employment.

That’s a fair argument.

Markets are extraordinarily effective at allocating capital.

But history also shows that periods of profound technological transformation often require new institutions.

Industrialization eventually gave rise to universal public education.

Social Security.

The GI Bill.

Community colleges.

The interstate highway system.

Each emerged because Americans recognized that rapid economic change demanded new ways of preparing people for a different future.

Agentic AI may represent another such moment.

Let me be clear: this isn’t an argument for bigger government.

Nor is it an argument for reviving New Deal programs exactly as they once existed.

It’s an argument for supporting unbridled imagination and experimentation.

If AI permanently reshapes the labor market, then, as stakeholders, we should begin designing resilient systems that help people build lives of purpose and economic success.

I’ve spent the past several years writing about Work Fit—the idea that work should align with who we are, how we want to live, and the contributions we hope to make.

But Work Fit requires more than personal agency.

It requires opportunity.

And perhaps that is the real challenge before us.

Not simply helping people find meaningful work.

But ensuring that meaningful work still exists as the doorway into adulthood.

If that doorway narrows dramatically over the next decade, the question won’t be whether America should recreate the New Deal.

The better question will be this:

What should America build next so that our newest workplace entrants still have the chance to work and build meaningful lives?

Even in these difficult moments, I believe that our best days lie ahead.

Note: image in this post was rendered on ChatGPT based on our prompts.

I’m Dan Smolen. As host and executive producer of What’s Your Work Fit? I help you make your work and workplace decisions result in better and more satisfying professional experiences and outcomes. I am also a Founding Member of The Future of Work Alliance.

Join What's Your Work Fit? on LinkedIn

Join What's Your Work Fit? on YouTube

Since the onset of the Industrial Revolution, management has been built around a deceptively simple question:

How do we squeeze more output from the people we already employ?

Every new technology—from the assembly line to the personal computer—has largely been judged by one measure: productivity. Could it help workers produce more output, more quickly, and at lower cost?

Agentic AI is no exception. Across industries, leaders are celebrating the increased productivity it can achieve.

Yet, at 2 o’clock this morning, I woke up wondering if we’re asking the wrong question.

Perhaps the real question isn’t:

How much more work can people do because of AI?

Perhaps it’s this:

What should organizations do with the time that AI gives back?

That may become one of the defining leadership questions of the AI era.

Two Companies, Two Futures

Imagine two companies.

Both adopt the same Agentic AI tools. Both discover that knowledge workers recover on average five hours each week.

The first company’s leadership immediately sees an opportunity.

“Excellent,” they say. “Now everyone can take on more work.”

At the second company, leadership pauses.

“What if,” they ask, “we invested some of those five hours in helping our people become better at what only humans can do?”

The first company extracts more productivity. The second builds greater productive capacity.

At first glance, those ideas sound similar.

They’re not.

Productivity Is Output. Capacity Is Potential.

Capacity grows when people deepen their judgment, strengthen relationships, learn new skills, mentor colleagues, ask better questions, exercise discernment, and discover better ways to create value.

Unlike productivity, capacity compounds.

Every new capability makes future capabilities easier to develop.

That’s how organizations become more resilient, innovative, and adaptable.

The Industrial Age Mindset Still Shapes Modern Leadership

Much of modern management traces its roots to the Industrial Revolution.

Factories sought consistency. Managers optimized workflows. Efficiency became the highest virtue.

That approach made perfect sense for repetitive work.

But knowledge work has never fit neatly into that model.

And Agentic AI is widening the gap.

As Agentic AI increasingly performs routine analysis, documentation, coding assistance, scheduling, summarization, and countless administrative tasks, human work changes.

The scarce resource is no longer the ability to process information.

Information has become abundant.

Judgment has become scarce.

Discernment has become scarce.

Trust has become scarce.

Creativity has become scarce.

Leadership has become scarcer still.

Those capabilities cannot simply be demanded through longer workdays or fuller calendars.

They require time to develop.

The Hidden Cost of Extraction

Too often, organizations mistake extraction for performance.

A leader produces record quarterly numbers.

The c-suite’s members applaud.

But what happened beneath those numbers?

Employees skipped vacations.

Managers carried impossible workloads.

High performers quietly updated their résumés.

Mentoring disappeared.

Learning stopped.

Innovation slowed.

Relationships frayed.

The next quarter looked impressive because the organization borrowed against its future.

We rarely describe it that way.

We call it “driving results.”

But there is another way to see it.

Some leaders don’t create performance. They borrow it from tomorrow.

Every exhausted employee, every preventable resignation, every burned-out manager represents capacity that has been spent faster than it can be replenished.

That may satisfy this quarter’s earnings report.

It rarely strengthens future opportunity.

AI Has Created Investable Human Time

This is where AI changes everything.

For the first time in generations, many organizations will gain meaningful amounts of human time.

The temptation will be obvious.

Fill every available minute.

Assign another project.

Increase utilization.

Raise performance expectations once again.

But reclaimed time is more than extra capacity to produce. It is an opportunity to invest.

Companies routinely decide what to do with financial profits.

Some distribute them immediately.

Others reinvest them to create future growth.

Why shouldn’t leaders think about reclaimed human time the same way?

What if some of AI’s greatest return came not from producing more work today, but from producing more capable people tomorrow?

Imagine using those recovered hours to coach emerging leaders.

To help employees master difficult conversations.

To encourage experimentation and iteration.

To deepen customer understanding.

To strengthen cross-functional collaboration.

To develop discernment..

Those investments don’t simply increase productivity. They increase productive capacity.

Work Fit Can Enter the Conversation in a Profound Way

I believe Work Fit becomes more than a career philosophy. It becomes an economic strategy.

People who experience stronger Work Fit are more likely to remain with an organization.

They continue learning.

They develop institutional knowledge.

They build trusted relationships.

They mentor newer colleagues.

They exercise sound judgment under pressure.

They contribute ideas because they feel connected to the work rather than trapped by it.

Not every employee will have perfect Work Fit.

No organization can satisfy every personal preference.

Nor should it.

However, leaders who intentionally help people move toward better alignment between their strengths, their responsibilities, and their aspirations aren’t merely improving employee satisfaction.

They’re investing in the organization’s future productive capacity.

That’s a very different conversation.

The Next Competitive Advantage

For decades we’ve measured leadership by asking one question:

How much added output did your team produce?

The Agentic AI era invites a better question.

How much more capable did your team become under your leadership?

Those aren’t the same thing.

One measures what people accomplished.

The other measures what they are becoming.

I suspect the organizations that thrive over the next decade won’t simply be the ones that deploy the best Agentic AI.

They’ll be the ones whose leaders understand that technology should expand human capability, not merely intensify human workload.

AI has given us a remarkable opportunity. Not simply to produce more. But to become more.

The companies that recognize the difference won’t just build more productive organizations.

They’ll build organizations whose productive capacity grows year after year because the people in their workplace thrive.

Perhaps that is the real promise of AI—not replacing human work, but creating the time and space for humans to become better at the work only they can do.

Our best days lie ahead.

Note: image in this post was rendered on ChatGPT based on our prompts.

I’m Dan Smolen. As host and executive producer of What’s Your Work Fit? I help you make your work and workplace decisions result in better and more satisfying professional experiences and outcomes. I am also a Founding Member of The Future of Work Alliance.

Join What's Your Work Fit? on LinkedIn

Join What's Your Work Fit? on YouTube

This week, I’ve argued that AI is making intelligence more abundant while elevating the value of discernment, reasoning, and judgment.

If machines increasingly handle information processing, then humans become more valuable for the uniquely human work of asking better questions, navigating ambiguity, mentoring others, and making wise decisions.

But there’s another future that deserves equal attention. It is a future in which none of that happens.

Instead, AI makes people faster—and leadership simply asks them to do more.

That’s the uncomfortable conversation we need to have.

Because AI is creating something that organizations have rarely known how to manage well:

Human time.

History suggests we already know the answer.

For more than two centuries, every major productivity breakthrough has carried the same promise.

Workers will have more time.

Sometimes they did.

More often, organizations simply found new ways to consume it.

The assembly line increased output. We built more products.

Email accelerated communication. We sent exponentially more messages.

Smartphones untethered work from the office. We quietly extended the workday.

Now AI summarizes reports, prepares presentations, analyzes data, drafts proposals, writes software, and automates countless administrative tasks.

Many executives will see those efficiencies not as liberated capacity—but as unused capacity.

Unused capacity looks, on a spreadsheet, like waste.

That mindset could become AI’s greatest unintended consequence.

The AI Productivity Trap

Imagine an employee who previously completed five major projects each month.

AI now enables six.

The industrial reflex asks a simple question:

“Why not eight?”

Soon the organization isn’t celebrating the quality of work.

It’s measuring throughput.

Dashboards improve.

Quarterly earnings look stronger.

Shareholders applaud.

Employees quietly wonder why they’re more exhausted than they were before AI arrived.

The technology that promised freedom instead becomes another mechanism for acceleration.

The work becomes faster.

The people do not necessarily become better.

AI Should Expand People, Not Just Output

The greatest opportunity AI creates isn’t higher productivity. It’s higher human capability.

Imagine a workplace where the time recovered through automation is intentionally reinvested.

Not into more repetitive assignments.

Into better work.

Time to mentor younger colleagues.

Time to build relationships with customers.

Time to experiment.

Time to solve problems that have sat untouched for years.

Time to learn entirely new skills.

Time to collaborate across departments.

Time to reflect before making consequential decisions.

These aren’t idle luxuries.

They’re the very activities that create resilience inside organizations.

Ironically, they’re also the first things eliminated when everyone becomes too busy.

The Hidden Cost of “More”

There’s a business argument here that rarely appears in quarterly reports. Replacing experienced employees is expensive.

Organizations pay for recruiting.

They pay for onboarding.

They lose institutional knowledge.

Managers devote months to bringing replacements up to speed.

Customers notice inconsistency.

Teams lose cohesion.

None of those costs show up neatly beside the productivity gains AI delivered.

But they’re real.

A company that squeezes another 15 percent of productivity from its workforce while doubling burnout hasn’t necessarily created value. It may simply have delayed paying the bill.

Disengagement compounds quietly.

By the time leaders notice it, the best people have often already left.

Perhaps That’s the Strategy

There’s another possibility we should consider.

In that model, institutional loyalty matters less.

Knowledge becomes modular.

Work becomes increasingly transactional.

The question shifts from “How do we develop this person over twenty years?” to “How quickly can someone contribute before the project ends?”

That future isn’t inherently good or bad.

For many professionals, being “a 1099” offers greater flexibility and agency.

For many businesses, it creates agility.

But it changes the social contract of work.

It also places a greater burden on individuals to continuously learn, adapt, and manage their own careers.

If organizations invest less in developing people, people will have to invest far more in developing themselves.

The Companies That Will Stand Apart

Imagine interviewing candidates with a message like this:

“We don’t use AI so you’ll do twice as much work.

We use AI so you’ll do work that’s twice as meaningful.”

That’s a radically different employer value proposition.

It attracts people who want to grow.

It retains people who feel trusted.

It develops leaders instead of task managers.

And over time, it builds cultures capable of adapting to whatever comes next.

Because employees remember how organizations made them feel.

Not how efficiently they cleared their inbox.

A Leadership Decision, Not a Technology Decision

None of this will be determined by OpenAI, Anthropic, Microsoft, or Google.

It will be determined within the walls of executive conference rooms.

Leadership teams will decide whether AI creates better work or simply more work.

One path treats human time as excess capacity waiting to be filled.

The other treats human time as an investment waiting to be made.

One optimizes labor.

The other develops people.

Those are profoundly different philosophies. And they will produce profoundly different workplaces.

The Work Fit Question

That answer may tell us almost everything we need to know about the future of work.

Because AI is not merely changing how work gets done.

It is forcing every organization to reveal what it truly believes people are for.

Our best days lie ahead.

Note: image in this post was rendered on ChatGPT based on our prompts.

I’m Dan Smolen. As host and executive producer of What’s Your Work Fit? I help you make your work and workplace decisions result in better and more satisfying professional experiences and outcomes. I am also a Founding Member of The Future of Work Alliance.

Join What's Your Work Fit? on LinkedIn

Join What's Your Work Fit? on YouTube

When I write on Substack that AI may help career professionals achieve greater Work Fit, I immediately get push back:

“That’s not what my boss is going to do.”

They’re probably right.

For all the excitement surrounding agentic AI, we have spent remarkably little time discussing what may become its greatest workplace consequence.

Not job displacement.

Not automation.

Not even productivity.

The real story may be what leaders choose to do with the human capacity AI creates.

Because AI doesn’t merely make work faster.

It creates surplus time.

And surplus time has always been management’s temptation.

Too often, they chose the latter.

Today, we stand at another one of those moments.

The New Productivity Trap

Imagine a marketing director who once needed a week to prepare a customer acquisition campaign.

Now AI helps her complete much of the work in a few days, perhaps in hours.

So, what happens next?

One possibility is that she uses the hours or days saved to think issues through more strategically, mentor younger colleagues, build stronger client relationships, or simply finish her workday in time to watch her daughter’s swim practice.

Another possibility?

She gets assigned three additional campaigns.

Nothing else changes except the volume.

Her workday remains ten hours long.

Only the expectations increase.

That’s not AI creating better work.

That’s AI becoming an accelerant for burnout.

Human Capacity Isn’t Infinite

For decades we’ve treated productivity as if it were the same thing as human capacity.

It isn’t.

People aren’t machines whose output scales indefinitely.

Even the most ambitious professionals have cognitive limits.

Attention fatigues.

Creativity diminishes.

Judgment deteriorates.

Relationships suffer.

And eventually, motivation disappears.

Ironically, the very technology that promises to reduce routine work could become the mechanism through which organizations quietly normalize even greater [worker] overload.

Who Will Be Most Tempted?

The greatest temptation may not come from leaders with bad intent. It may come from good leaders under extraordinary pressure.

In public companies chasing quarterly earning gains.

In organizations with shrinking operating budgets.

In nonprofits facing staffing freezes.

In private equity-owned firms seeking margin expansion.

In companies large and small that have spent years telling managers to “do more with less.”

If AI suddenly allows everyone to work 25 percent faster, some executives will inevitably ask a familiar question.

“What else can we give them?”

It’s an understandable business question.

It may also be the wrong one.

The Better Question

Suppose AI eliminates four hours of administrative work every week.

What if leaders didn’t immediately refill those four hours?

What if they asked instead:

What would make our people more thoughtful and discerning?

What would help them make better decisions?

How could they spend more time with customers and customer-prospects?

How might they mentor new hires?

Could they learn new skills?

Experiment?

Iterate?

Reflect?

Recover?

These activities often don’t show up neatly on productivity dashboards.

Yet they’re precisely the work that creates healthier organizations where people thrive.

AI Has Changed Leadership More Than It Has Changed Work

We often describe AI as a technology challenge. I think it’s rapidly becoming a leadership challenge.

Managers are no longer being asked simply to allocate work. They’re being asked to steward human capacity.

Those are very different responsibilities.

One assumes people exist primarily to maximize output.

The other recognizes that sustained excellence depends on energy, judgment, curiosity, and well-being.

The organizations that understand this distinction won’t merely retain better employees.

They’ll produce better work.

The Coming Great Divide

All that stated, I suspect AI will create two very different kinds of companies.

The first will use AI to squeeze more labor from fewer people.

Employees will become increasingly efficient, increasingly exhausted, and increasingly disengaged.

Turnover will rise.

Innovation will slow.

The first kind of company will mistake activity for progress.

The second kind of company will treat AI differently, perhaps reverentially.

It will use the efficiencies AI yields to give people back something that has been disappearing from work for decades:

Agency.

Time to think.

Time to learn.

Time to mentor.

Time to innovate.

Time to live.

Ironically, these companies may become the most productive of all—not because they demanded more from people, but because they finally understood what people are actually capable of when they’re trusted with their own capacity.

The Work Fit Question

Perhaps this is the real leadership test of the AI era.

When technology gives people back part of their day, who gets to decide what happens to it?

The answer may determine not only the future of productivity, but the future of Work Fit itself.

AI isn’t simply changing how we work. It’s forcing every organization to answer a far more consequential question:

Do we believe our people exist to maximize output?

Or do we believe that great work emerges when people have enough agency to become their very best selves?

Our best days lie ahead.

Note: image in this post was rendered on ChatGPT based on our prompts.

I’m Dan Smolen. As host and executive producer of What’s Your Work Fit? I help you make your work and workplace decisions result in better and more satisfying professional experiences and outcomes. I am also a Founding Member of The Future of Work Alliance.

Join What's Your Work Fit? on LinkedIn

Join What's Your Work Fit? on YouTube

Since the onset of the Industrial Revolution, work has been measured in time.

Forty-hour weeks.
Eight-hour days.
Billable hours.
Time clocks.
Utilization rates.
Productivity dashboards.

Even when knowledge work replaced factory work, remarkably little changed. We simply traded assembly lines for Outlook calendar entries. Instead of punching a time-clock, we accepted back-to-back meetings. Instead of supervisors walking the shop floor, software monitored our activity and calendars dictated our days.

We still rented our hours to someone else.

But agentic AI changes something fundamental.

Not because it works faster.

Because it works while we don’t.

We have mistaken activity for value.

For decades we’ve celebrated busyness.

Busy people must be important.

Busy calendars must mean productive organizations.

Long workdays must signal commitment.

But anyone who has spent time inside a modern organization knows better.

Hours disappear into status meetings.

Slide decks.

Email chains.

Approvals.

Reports written mostly because someone above us asked for them.

Entire careers have been built around coordinating work rather than creating it.

Agentic AI has little interest in these rituals.

It doesn’t care who is copied on an email.

It doesn’t need meetings to align itself.

It doesn’t suffer from decision fatigue after the seventh Zoom call.

It simply executes.

Which raises an uncomfortable question.

Time may become the new measure of privilege.

Today, wealth often determines who controls their schedule.

CEOs enjoy flexibility.

Partners delegate.

Entrepreneurs choose their hours.

Frontline workers rarely do.

But what if AI dramatically expands everyone’s productive capacity?

Will organizations return those hours to people? Or will they simply expect more output during the same workday?

History offers reasons for skepticism.

Technology has repeatedly promised leisure.

Instead, we’ve often filled the newfound capacity with additional work.

Email didn’t reduce correspondence.

It multiplied it.

Smartphones didn’t free us from the office.

They brought the office into our kitchens.

Video meetings didn’t reduce meetings.

They made it easier to schedule even more.

Perhaps AI will do the same.

Or perhaps this time really is different.

The next workplace battle may not be over jobs. It may be over time.

Imagine two organizations.

The first uses AI to eliminate repetitive work.

Employees leave earlier.

They spend more time mentoring.

Learning.

Thinking.

Creating.

Volunteering.

Being parents.

Being neighbors.

Being fully human.

The second organization also adopts AI.

Leadership expects twice the output.

Twice the responsiveness.

Twice the projects.

Employees become supervisors of tireless digital workers who never stop producing.

Which workplace attracts better people?

Which produces wiser decisions?

Which sustains creativity over decades?

The competition for talent may increasingly become a competition over who gives people greater sovereignty over their own time.

Leadership may become stewardship of time.

Industrial management optimized labor.

Knowledge management optimized information.

AI-era leadership may optimize something else entirely:

Human attention.

Judgment.

Relationships.

Meaning.

The best managers may soon ask a radically different question.

Not:

“How do I keep everyone busy?”

But:

“What work is uniquely worthy of human time?”

That’s a profound shift.

Because once AI performs much of the routine work, every remaining human hour becomes more valuable, not less.

Every meeting has to justify itself.

Every interruption has to earn its place.

Every project has to deserve a person’s attention.

Perhaps leadership’s greatest responsibility won’t be assigning work. It will be protecting time.

That’s How Work Fit Becomes Time Fit.

I’ve long argued that people shouldn’t organize their lives around jobs.

They should organize them around Work Fit.

Agentic AI adds another layer.

We ask:

“What’s your Work Fit?”

A follow-on to that query may need to be:

“And, who gets to decide how your time is spent?”

Because time is our only truly non-renewable resource.

Money returns.

Jobs return.

Companies come and go.

People can embark on new career acts.

But every hour we surrender is gone forever.

If AI gives humanity anything extraordinary, I hope it isn’t merely higher productivity.

I hope it gives us the chance to reclaim ownership of our time.

Not to work less for the sake of working less.

But to spend more of our finite hours on the things only humans can do:

Love deeply.

Raise children.

Build enduring friendships.

Mentor the next generation.

Create beauty.

Serve our communities.

Ask wiser questions.

And perhaps, finally, become the authors of our own days.

When that happens, our best days lie ahead.

Note: image in this post was rendered on ChatGPT based on our prompts.

I’m Dan Smolen. As host and executive producer of What’s Your Work Fit? I help you make your work and workplace decisions result in better and more satisfying professional experiences and outcomes. I am also a Founding Member of The Future of Work Alliance.

Join What's Your Work Fit? on LinkedIn

Join What's Your Work Fit? on YouTube